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Background and PurposeCannabidiol (CBD) is used clinically as an anticonvulsant. Its precise mechanism of action has remained unclear. CBD was recently demonstrated to enhance the activity of the neuronal K(V)7.2/7.3 channel, which may be one important contributor to CBD anticonvulsant effect. Curiously, CBD inhibits the closely related cardiac K(V)7.1/KCNE1 channel. Whether and how CBD affects other K(V)7 subtypes remains uninvestigated and the CBD interaction sites mediating these diverse effects remain unknown. Experimental ApproachHere, we used electrophysiology, molecular dynamics simulations, molecular docking and site-directed mutagenesis to address these questions. Key ResultsWe found that CBD modulates the activity of all human K(V)7 subtypes and that the effects are subtype dependent. CBD enhanced the activity of K(V)7.2-7.5 subtypes, seen as a V-50 shift towards more negative voltages or increased maximum conductance. In contrast, CBD inhibited the K(V)7.1 and K(V)7.1/KCNE1 channels, seen as a V-50 shift towards more positive voltages and reduced conductance. In K(V)7.2 and K(V)7.4, we propose a CBD interaction site at the subunit interface in the pore domain that overlaps with the interaction site of other compounds, notably the anticonvulsant retigabine. However, CBD relies on other residues for its effects than the conserved tryptophan that is critical for retigabine effects. We propose a similar, though not identical CBD site in K(V)7.1, with a non-conserved phenylalanine being important. Conclusions and ImplicationsWe identify novel targets of CBD, contributing to a better understanding of CBD clinical effects and provide mechanistic insights into how CBD modulates different K(V)7 subtypes., Funding Agencies|Goeran Gustafsson Foundation; European Research Council (ERC) [850622]; Swedish Research Council [VR2018-04905, VR2021-01885]; Science for Life Laboratory; Marie-Sklodowska Curie Fellowship Lipopeutics [898762]

Many new psychoactive substances (NPS) are stimulants, and information about their potency and abuse potential is often lacking. To start addressing this need, a method measuring the inhibition of the dopamine, serotonin, and norepinephrine transporters (DAT, SERT, and NET) by stimulant drugs was developed. The use of a proprietary fluorescent dye mixture and three cell lines (CHO-K1, HEK 293, and MDCK), each expressing a single transporter, allowed for a semiautomated, one-pot determination of inhibition in a 384-well format. The method was validated using well characterized stimulants, including cocaine, amphetamine, 3,4-methylenedioxymethamphetamine (MDMA), & alpha;-PVP, and fluoxetine and performed similarly to other methods. Seven synthetic cathinones all showed highest potency for DAT inhibition, followed by NET and SERT. The rank potency for DAT inhibition IC50 (nM) was MPHP (4.53) > 4Cl-& alpha;-PVP (8.05) > 3F-& alpha;-PVP (12.7) > & alpha;-PiHP (13.4) > N-ethylpentylone (16.9) > N-ethylhexedrone (44.5) > 4-methylpentedrone (261). All but 4-methylpentedrone were more potent than amphetamine (257) and cocaine (111). The DAT/SERT inhibition ratio for the cathinones was in the range from 5.02 for 4-methylpentedrone to >3730 for & alpha;-PiHP, compared to 1.64 for cocaine and >4030 for & alpha;-PVP. All seven substances had inhibition profiles similar to those of potent stimulants with high abuse potential., Funding Agencies|Public Health Agency of Sweden; National Board of Forensic Medicine

The usage of nanomaterials for rheumatoid arthritis (RA) treatment can improve bioavailability and enable selective targeting. The current study prepares and evaluates the in vivo biological effects of a novel hydroxyapatite/vitamin B-12 nanoformula in Complete Freunds adjuvant-induced arthritis in rats. The synthesized nanoformula was characterized using XRD, FTIR, BET analysis, HERTEM, SEM, particle size, and zeta potential. We synthesized pure HAP NPs with 71.01% loading weight percentages of Vit B12 and 49 mg/g loading capacity. Loading of vitamin B-12 on hydroxyapatite was modeled by Monte Carlo simulation. Anti-arthritic, anti-inflammatory, and antioxidant effects of the prepared nanoformula were assessed. Treated arthritic rats showed lower levels of RF and CRP, IL-1 beta, TNF-alpha, IL-17, and ADAMTS-5, but higher IL-4 and TIMP-3 levels. In addition, the prepared nanoformula increased GSH content and GST antioxidant activity while decreasing LPO levels. Furthermore, it reduced the expression of TGF-beta mRNA. Histopathological examinations revealed an improvement in joint injuries through the reduction of inflammatory cell infiltration, cartilage deterioration, and bone damage caused by Complete Freunds adjuvant. These findings indicate that the anti-arthritic, antioxidant, and anti-inflammatory properties of the prepared nanoformula could be useful for the development of new anti-arthritic treatments., Funding Agencies|King Salman Center for Disability Research [KSRG-2022-047]

Abstrakt Introduktion och syfte: Löslighet är en av de viktigaste parametrarna som spelar en viktig roll vid upptäckt och utveckling av läkemedel. Att ha kännedom om löslighet av något läkemedel är viktigt eftersom det hjälper till att formulera nya läkemedelskandidater. Olika teoretiska metoder används för att förutsäga löslighet, såsom kvantitativ struktur-egenskapsrelation (QSPR- modeller). Maskininlärning är ett ytterligare sätt att förutsäga löslighet. Syftet med denna studie är att göra en litteraturgenomgång för att fastställa hur olika maskininlärningsmodeller används för att förutsäga löslighet. Syftet är också att ta reda på om det finns trender i prestanda för olika maskininlärningsalgoritmer, samt trender i hur användningen av datasetet påverkar prestanda. Metod: En litteratursökning fokuserad på användning av maskininlärning för att förutsäga löslighet genomfördes. PubMed och Google användes för att söka efter vetenskapliga artiklar. De olika maskininlärningsalgoritmerna delades in i fem olika kategorier. Dessutom bestämdes fördelningen mellan användandet av regression och klassificering vad gäller löslighetsprediktion. En intressant parameter att studera är hur storleken på felet varierar med storleken på dataseten. Det är också viktigt att veta hur storleken på felet beror på antalet deskriptorer. Slutligen undersökte denna studie hur storleken på felet varierar med andelen molekyler i ett dataset som används för träning, test och validering. Resultat: Studien visar att både beslutsträd och neurala nätverk används mest i artiklar, och att beslutsträd har ett högre R2-värde än neurala nätverk. Bayesianska metoder förefaller också ge goda resultat, men används inte lika mycket som andra metoder. De flesta artiklar använder regression för att erhålla en kontinuerlig variabel såsom löslighet. Ju större dataset, desto mindre felstorlek och ett bättre resultat. För

Antibiotic resistance is becoming an accelerating issue, both in the treatment of nosocomial infections and for public health in general. There is a pressing need for innovative methods to understand how available antibiotics could be combined to overcome and limit the emergence of resistance. Pharmacokinetic-pharmacodynamic (PKPD) modelling and simulation (M&S) is a promising tool in quantitatively characterising longitudinal antibiotic drug effects, especially in exploring antibiotic combinations where the concentration ratios of the component drugs, their interactions and relative contributions to the combined drug effects change over time. This thesis aimed to develop approaches to quantify the effects of antibiotic combinations on gram-negative bacteria by PKPD M&S. Pre-clinical data for antibiotics alone and in combination, including in vitro static and dynamic time-kill data and in vivo murine thigh infection data, were quantitatively described by semi-mechanistic PKPD models, as exemplified in the thesis by the combination of colistin and ciprofloxacin against four Escherichia coli strains and by the combination of polymyxin B and minocycline against two Klebsiella pneumoniae strains. The developed PKPD models showed good translational ability, as demonstrated by bridging across strains, predicting in vitro drug effects under dynamic settings, integrating all available in vitro and in vivo data, and quantifying the discrepancy between the in vitro and in vivo settings. These developed models were used to explore the therapeutic potential of the combinations. The value of adding low dosages of colistin (thus low toxicity) to ciprofloxacin treatment for E. coli strains with decreased susceptibility, and of combining polymyxin B and minocycline against K. pneumoniae when either drug alone has a limited antibacterial effect, was shown. In addition, a methodological framework was developed to quantify antibiotic effects from time-lapse microscopic images

Therapeutic dose of a medication refers to the quantity of a drug required to produce a pharmacological effect without causing unacceptable adverse events. Dose selection in the clinical setting is not straight forward due to various factors, including specific patient factors such as age, sex, weight, genetic variants and renal/hepatic function, as well as external factors such as food and co-medication, all of which can influence the efficacy and safety of a drug. In silico mechanistic mathematical models, such as physiologically-based pharmacokinetic (PBPK) models, can aid dose setting by considering all these factors into a mechanistic model. The aim of this thesis was to enhance the application of PBPK modelling in drug development, with specific focus on drug-drug interactions and evaluations of genotype and disease dependencies. Additionally, the thesis assessed the implications of pre-defined PBPK platform differences. PBPK modelling, in conjunction with in vitro generated data, quantitative proteomics, pharmacodynamic models, and/or chronic kidney disease (CKD) population models, were applied to predict drug interactions in various clinically relevant cases. The model drugs used were statins, itraconazole and roxadustat. Additionally, two different PBPK platforms, Simcyp and PK-Sim, were employed throughout the different projects of this thesis providing insights on platform differences and its potential implications. The generated in vitro data and developed PBPK models provided novel insights into: 1) the relative contribution of itraconazole metabolites to the clinical drug-drug interactions, 2) the impact of statins’ distribution to muscle by transporters, 3) the critical pathways involved in the effects of disease on statins and roxadustat pharmacokinetics and drug interactions, 4) the influence of disease not only on statins’ pharmacokinetics but also on their efficacy and safety, 5) differences in software and potential implications when selecting

Polymers are a group of macromolecules used in formulations of pharmaceuticals, one example being the delivery system DC Bead™. Further, some of the most abundant and for drug delivery important constituents of the subcutaneous tissue are charged polymers (polyelectrolytes), e.g. collagen, hyaluronic acid, and chondroitin sulfate. The interactions between these subcutaneous polyelectrolytes and drug molecules are believed to heavily affect the transport and absorption of subcutaneously injected drugs. To increase the understanding of how the interactions between subcutaneous polymers and drug molecules affect the pharmaceutical behavior in subcutaneous tissue, we developed a new microfluidic-based platform. The platform is used to study interactions between polyelectrolytes and drug molecules, and can beyond the investigation of subcutaneous interactions be used to develop polyelectrolyte-based microgel formulations. In this thesis, the microfluidic method denoted “Microfluidic chip for interactions studies” (MIS) is presented, and the design, validation, and several examples of usage are described. The method which is based on microfluidic instrumentation, utilizes spherical microgels created using different types of polymers/polyelectrolytes. These hydrogels collapse when experiencing attractive interactions with drug molecules making it possible to investigate drug binding by studying the volume change of the microgels. We prove that the interactions are strongly affected by charges both on the gel networks and the drug molecules. Further, the aggregation behavior of drugs in a polyelectrolyte-rich environment is studied in detail. Results show that both a strong aggregation behavior and a high charge on the drugs may affect the transport through a network of polyelectrolytes. The behavior of drugs in subcutaneous polyelectrolyte-rich environments such as hyaluronic acid networks, can partly explain bioavailability and absorption rates of the drugs in vivo. Sever

Background and PurposeCannabidiol (CBD) is used clinically as an anticonvulsant. Its precise mechanism of action has remained unclear. CBD was recently demonstrated to enhance the activity of the neuronal K(V)7.2/7.3 channel, which may be one important contributor to CBD anticonvulsant effect. Curiously, CBD inhibits the closely related cardiac K(V)7.1/KCNE1 channel. Whether and how CBD affects other K(V)7 subtypes remains uninvestigated and the CBD interaction sites mediating these diverse effects remain unknown. Experimental ApproachHere, we used electrophysiology, molecular dynamics simulations, molecular docking and site-directed mutagenesis to address these questions. Key ResultsWe found that CBD modulates the activity of all human K(V)7 subtypes and that the effects are subtype dependent. CBD enhanced the activity of K(V)7.2-7.5 subtypes, seen as a V-50 shift towards more negative voltages or increased maximum conductance. In contrast, CBD inhibited the K(V)7.1 and K(V)7.1/KCNE1 channels, seen as a V-50 shift towards more positive voltages and reduced conductance. In K(V)7.2 and K(V)7.4, we propose a CBD interaction site at the subunit interface in the pore domain that overlaps with the interaction site of other compounds, notably the anticonvulsant retigabine. However, CBD relies on other residues for its effects than the conserved tryptophan that is critical for retigabine effects. We propose a similar, though not identical CBD site in K(V)7.1, with a non-conserved phenylalanine being important. Conclusions and ImplicationsWe identify novel targets of CBD, contributing to a better understanding of CBD clinical effects and provide mechanistic insights into how CBD modulates different K(V)7 subtypes., Funding Agencies|Goeran Gustafsson Foundation; European Research Council (ERC) [850622]; Swedish Research Council [VR2018-04905, VR2021-01885]; Science for Life Laboratory; Marie-Sklodowska Curie Fellowship Lipopeutics [898762]

Many new psychoactive substances (NPS) are stimulants, and information about their potency and abuse potential is often lacking. To start addressing this need, a method measuring the inhibition of the dopamine, serotonin, and norepinephrine transporters (DAT, SERT, and NET) by stimulant drugs was developed. The use of a proprietary fluorescent dye mixture and three cell lines (CHO-K1, HEK 293, and MDCK), each expressing a single transporter, allowed for a semiautomated, one-pot determination of inhibition in a 384-well format. The method was validated using well characterized stimulants, including cocaine, amphetamine, 3,4-methylenedioxymethamphetamine (MDMA), & alpha;-PVP, and fluoxetine and performed similarly to other methods. Seven synthetic cathinones all showed highest potency for DAT inhibition, followed by NET and SERT. The rank potency for DAT inhibition IC50 (nM) was MPHP (4.53) > 4Cl-& alpha;-PVP (8.05) > 3F-& alpha;-PVP (12.7) > & alpha;-PiHP (13.4) > N-ethylpentylone (16.9) > N-ethylhexedrone (44.5) > 4-methylpentedrone (261). All but 4-methylpentedrone were more potent than amphetamine (257) and cocaine (111). The DAT/SERT inhibition ratio for the cathinones was in the range from 5.02 for 4-methylpentedrone to >3730 for & alpha;-PiHP, compared to 1.64 for cocaine and >4030 for & alpha;-PVP. All seven substances had inhibition profiles similar to those of potent stimulants with high abuse potential., Funding Agencies|Public Health Agency of Sweden; National Board of Forensic Medicine

Disease progression in nonalcoholic steatohepatitis (NASH) is highly heterogenous and remains poorly understood. Fibrosis stage is currently the best predictor for development of end-stage liver disease and mortality. Better understanding and quantifying the impact of factors affecting NASH and fibrosis is essential to inform a clinical study design. We developed a population Markov model to describe the transition probability between fibrosis stages and mortality using a unique clinical nonalcoholic fatty liver disease cohort with serial biopsies over 3 decades. We evaluated covariate effects on all model parameters and performed clinical trial simulations to predict the fibrosis progression rate for external clinical cohorts. All parameters were estimated with good precision. Age and diagnosis of type 2 diabetes (T2D) were found to be significant predictors in the model. Increase in hepatic steatosis between visits was the most important predictor for progression of fibrosis. Fibrosis progression rate (FPR) was twofold higher for fibrosis stages 0 and 1 (F0-1) compared to fibrosis stage 2 and 3 (F2-3). A twofold increase in FPR was observed for T2D. A two-point steatosis worsening increased the FPR 11-fold. Predicted fibrosis progression was in good agreement with data from external clinical cohorts. Our fibrosis progression model shows that patient selection, particularly initial fibrosis stage distribution, can significantly impact fibrosis progression and as such the window for assessing drug efficacy in clinical trials. Our work highlights the increase in hepatic steatosis as the most important factor in increasing FPR, emphasizing the importance of well-defined lifestyle advise for reducing variability in NASH progression during clinical trials.

Ocular surface neovascularization and its resulting pathological changes significantly alter corneal refraction and obstruct the light path to the retina, and hence is a major cause of vision loss. Various factors such as infection, irritation, trauma, dry eye, and ocular surface surgery trigger neovascularization via angiogenesis and lym-phangiogenesis dependent on VEGF-related and alternative mechanisms. Recent advances in antiangiogenic drugs, nanotechnology, gene therapy, surgical equipment and techniques, animal models, and drug delivery strategies have provided a range of novel therapeutic options for the treatment of ocular surfaceneovascularization. In this review article, we comprehensively discuss the etiology and mechanisms of corneal neovascularization and other types of ocular surface neovascularization, as well as emerging animal models and drug delivery strategies that facilitate its management., Funding Agencies|National Program on Key Research [2021YFA0804703]; National Youth Talent Support Program; Innovation Research Team of High-level Local Universities in Shanghai; Program for Professor of Special Appointment in Shanghai (Eastern Scholar) [TP2018007]; Longyan City Science and Technology Plan Project [2022LYF19075]; European Union [101007931]

Small-interfering ribonucleic acids (siRNAs) with N-acetylgalactosamine (GalNAc) conjugation for improved liver uptake represent an emerging class of drugs that modulate liver-expressed therapeutic targets. The pharmacokinetics of GalNAc-siRNAs are characterized by a rapid distribution from plasma to tissue (hours) and a long terminal plasma half-life, analyzed in the form of the antisense strand, driven by redistribution from tissue (weeks). Understanding how clinical pharmacokinetics relate to the dose and type of siRNA chemical stabilizing method used is critical, e.g., to design studies, to investigate safety windows, and to predict the pharmacokinetics of new preclinical assets. To this end, we collected and analyzed pharmacokinetic data from the literature regarding nine GalNAc-siRNAs. Based on this analysis, we showed that the clinical plasma pharmacokinetics of GalNAc-siRNAs are approximately dose proportional and similar between chemical stabilizing methods. This holds for both the area under the concentration-time curve (AUC) and the maximum plasma concentration (Cmax). Corresponding rat and monkey pharmacokinetic data for a subset of the nine GalNAc-siRNAs show dose-proportional Cmax, supra-dose-proportional AUC, and similar pharmacokinetics between chemical stabilizing methods . Together, the animal and human pharmacokinetic data indicate that plasma clearance divided by bioavailability follows allometric principles and scales between species with an exponent of 0.75. Finally, the clinical plasma concentration-time profiles can be empirically described by standard one-compartment kinetics with first-order absorption up to 24 h after subcutaneous dosing, and by three-compartment kinetics with first-order absorption in general. To describe the system more mechanistically, we report a corrected and unambiguously defined version of a previously published physiologically based pharmacokinetic model.

This article traces the historical development of various biomarkers of acute and/or chronic alcohol consumption. Much of the research in this domain of clinical and laboratory medicine arose from clinics and laboratories in Sweden, as exemplified by carbohydrate deficient transferrin (CDT) and phosphatidylethanol (PEth). Extensive studies of other alcohol biomarkers, such as ethyl glucuronide (EtG), ethyl sulfate (EtS), and 5-hydroxytryptophol (5-HTOL), also derive from Sweden. The most obvious test of recent drinking is identification of ethanol in a sample of the persons blood, breath, or urine. However, because of continuous metabolism in the liver, ethanol is eliminated from the blood at a rate of 0.15 g/L/h (range 0.1-0.3 g/L/h), so obtaining positive results is not always possible. The widow of detection is increased by analysis of ethanols non-oxidative metabolites (EtG and EtS), which are more slowly eliminated from the bloodstream. Likewise, an elevated ratio of serotonin metabolites in urine (5-HTOL/5-HIAA) can help to disclose recent drinking after ethanol is no longer measurable in body fluids. A highly specific biomarker of hazardous drinking is CDT, a serum glycoprotein (transferrin), with a deficiency in its N-linked glycosylation. Another widely acclaimed biomarker is PEth, an abnormal phospholipid synthesized in cell membranes when people drink excessively, having a long elimination half-life (median similar to 6 days) during abstinence. Research on the subject of alcohol biomarkers has increased appreciably and is now an important area of drug testing and analysis.

1-(2,3-Dihydro-1H-inden-5-yl)-2-(piperidin-1-yl)pentan-1-one (3,4-Pr-PipVP), a novel synthetic cathinone (SCat), was first identified in 2022 in Germany. The product was marketed as 1-(bicyclo[4.2.0]octa-1,3,5-trien-3-yl)-2-(pyrrolidin-1-yl)pentan-1-one (3,4-EtPV), a substance not covered by the German New Psychoactive Substances Act (NpSG). Although originally intended to be an exploratory new synthetic cathinone containing the novel bicyclo[4.2.0]octatrienyl function, the compound was subsequently confirmed to contain an indanyl ring system scheduled under generic legislation like the NpSG. However, it is one of only a few marketed SCats carrying a piperidine ring. Inhibition experiments involving norepinephrine, dopamine, and serotonin transporters showed that 3,4-Pr-PipVP was a low potency blocker at all three monoamine transporters compared to related substances such as MDPV. Additionally, pharmacokinetic data were collected from pooled human liver microsomes incubations and from the analysis of authentic urine samples received after oral administration of 5 mg 3,4-Pr-PipVP hydrochloride. Phase I metabolites were tentatively identified in vitro and in vivo using liquid chromatography-time-of-flight mass spectrometry. Main metabolites were formed by metabolic reduction of the carbonyl function with and without additional hydroxylations at the propylene bridge of the molecule. Keto-reduced H-2-3,4-Pr-PipVP and H-2-piperidine-OH-3,4-Pr-PipVP as well as aryl-OH-3,4-Pr-PipVP, and indanyl-OH-piperidine-OH-3,4-Pr-PipVP are suggested as most suitable biomarkers for the detection of 3,4-Pr-PipVP since they were detected for much longer than the parent compound. 3,4-Pr-PipVP could be detected for up to 21 h whereas its metabolites were detectable for up to about 4 days., Funding Agencies|Internal Security Fund of the European Union [IZ25-5793-2019-33]

Selective androgen receptor modulators (SARMs) such as ACP-105 are prohibited in sports due to their anabolic properties. ACP-105 has in previous equine studies shown to undergo extensive metabolism, which makes its metabolite profile important to investigate in humans, since the metabolism is unknown in this species. The aims of the study were to systematically optimize in vitro microsome incubations for improved metabolite yield and to utilize a multivariate data analysis (MVDA) approach to aid the metabolite discovery. Microsomes together with S9 fractions were used at optimal conditions, both with and without phase II additives. Furthermore, the relevance of the in vitro derived metabolites was evaluated as analytical targets in doping control by comparison with results from a human post-administration urine sample collected after a single dose of 100 µg ACP-105. All samples were analyzed with liquid chromatography - Orbitrap mass spectrometry. The use of the systematical optimization and MVDA greatly simplified the search and a total of 18 in vitro metabolites were tentatively identified. The yield of the two main monohydroxylated isomers increased by 24 and 10 times, respectively. In the human urine sample, a total of seven metabolites of ACP-105, formed by a combination of hydroxylations and glucuronic acid conjugations, were tentatively identified. The main metabolites were two monohydroxylated forms that are suggested as analytical targets for human doping control after hydrolysis. All the in vivo metabolites could be detected with the MVDA approach on the in vitro models, demonstrating its usefulness for prediction of the in vivo metabolite profile.

S-CE-123, a novel dopamine transporter inhibitor, has emerged as a potential candidate for cognitive enhancement. The objective of this study was to compare the tissue distribution profiles, with a specific focus on central nervous system distribution and metabolism, of S-CE-123 and R-modafinil. To address this objective, a precise liquid chromatography-high resolution mass spectrometry method was developed and partially validated. Neuropharmacokinetic parameters were assessed using the Combinatory Mapping Approach. Our findings reveal distinct differences between the two compounds. Notably, S-CE-123 demonstrates a significantly superior extent of transport across the blood-brain barrier (BBB), with an unbound brain-to-plasma concentration ratio (K-p,K-uu,K-brain) of 0.5, compared to R-modafinil's K-p,K-uu,K-brain of 0.1. A similar pattern was observed for the transport across the blood-spinal cord barrier. Concerning the drug transport across cellular membranes, we observed that S-CE-123 primarily localizes in the brain interstitial space, whereas R-modafinil distributes more evenly across both sides of the plasma membrane of the brain's parenchymal cells (K-p,K-uu,K-cell). Furthermore, our study highlights the substantial differences in hepatic metabolic stability, with S-CE-123 having a 9.3-fold faster metabolism compared to R-modafinil. In summary, the combination of improved BBB transport and higher affinity of S-CE-123 to dopamine transporters in comparison to R-modafinil makes S-CE-123 a promising candidate for further testing for the treatment of cognitive decline.

Therapeutic dose of a medication refers to the quantity of a drug required to produce a pharmacological effect without causing unacceptable adverse events. Dose selection in the clinical setting is not straight forward due to various factors, including specific patient factors such as age, sex, weight, genetic variants and renal/hepatic function, as well as external factors such as food and co-medication, all of which can influence the efficacy and safety of a drug. In silico mechanistic mathematical models, such as physiologically-based pharmacokinetic (PBPK) models, can aid dose setting by considering all these factors into a mechanistic model. The aim of this thesis was to enhance the application of PBPK modelling in drug development, with specific focus on drug-drug interactions and evaluations of genotype and disease dependencies. Additionally, the thesis assessed the implications of pre-defined PBPK platform differences. PBPK modelling, in conjunction with in vitro generated data, quantitative proteomics, pharmacodynamic models, and/or chronic kidney disease (CKD) population models, were applied to predict drug interactions in various clinically relevant cases. The model drugs used were statins, itraconazole and roxadustat. Additionally, two different PBPK platforms, Simcyp and PK-Sim, were employed throughout the different projects of this thesis providing insights on platform differences and its potential implications. The generated in vitro data and developed PBPK models provided novel insights into: 1) the relative contribution of itraconazole metabolites to the clinical drug-drug interactions, 2) the impact of statins’ distribution to muscle by transporters, 3) the critical pathways involved in the effects of disease on statins and roxadustat pharmacokinetics and drug interactions, 4) the influence of disease not only on statins’ pharmacokinetics but also on their efficacy and safety, 5) differences in software and potential implications when selecting

Ibrutinib is a Bruton tyrosine kinase (Btk) inhibitor for treating chronic lymphocytic leukemia (CLL). It has also been associated with hypertension. The optimal dosing schedule for mitigating this adverse effect is currently under discussion. A quantification of relationships between systemic ibrutinib exposure and efficacy (i.e., leukocyte count and sum of the product of perpendicular diameters [SPD] of lymph nodes) and hypertension toxicity (i.e., blood pressure), and their association with overall survival is needed. Here, we present a semi-mechanistic pharmacokinetic-pharmacodynamic modeling framework to characterize such relationships and facilitate dose optimization. Data from a phase Ib/II study were used, including ibrutinib plasma concentrations to derive daily 0-24-h area under the concentration-time curve, leukocyte count, SPD, survival, and blood pressure measurements. A nonlinear mixed effects modeling approach was applied, considering ibrutinib's pharmacological action and CLL cell dynamics. The final framework included (i) an integrated model for SPD and leukocytes consisting of four CLL cell subpopulations with ibrutinib inhibiting phosphorylated Btk production, ( ii) a turnover model in which ibrutinib stimulates an increase in blood pressure, and (iii) a competing risk model for dropout and death. Simulations predicted that the approved dosing schedule had a slightly higher efficacy (24-month, progression- free survival [PFS] 98%) than de-escalation schedules (24-month, average PFS approximate to 97%); the latter had, on average, approximate to 20% lower proportions of patients with hypertension. The developed modeling framework offers an improved understanding of the relationships among ibrutinib exposure, efficacy and toxicity biomarkers. This framework can serve as a platform to assess dosing schedules in a biologically plausible manner.

Protein drugs, such as monoclonal antibodies, have proved successful in treating cancer and immune system diseases. The structural complexity of these molecules requires careful handling to ensure integrity and stability of the drug. In this study, a failure mode and effects analysis was performed based on a Gemba Walk method in a Swedish University Hospital. The Gemba Walk is focused on pharmacists observing the actual supply process steps from distributor, pharmacy cleanroom to patient administration. Relevant protein drugs are chosen based on sales statistics within the hospital and the corresponding wards were observed. Further is the Double Diamond design method used to identify major risks and deliver mitigation strategies. The study identified potential stress factors such as temperature, shock by impact, shaking, vibration and light exposure. There were also risks associated with porters' and healthcare professionals' lack of awareness and access to information. These risk factors may cause loss of efficacy and quality of the protein drug, potentially leading to patient safety concerns. In this study, a simulation is also performed to list measures that theoretically should be in place to ensure the quality of the protein drug, for example validated and protocol-based compounding in cleanroom, training and validated transports., RealHOPE

Large clinical trials often generate complex and large datasets which need to be presented frequently throughout the trial for interim analysis or to inform a data safety monitory board (DSMB). In addition, reliable and traceability are required to ensure reproducibility in pharmacometric data analysis. A reproducible pharmacometric analysis workflow was developed during a large clinical trial involving 1000 participants over one year testing Bacillus Calmette-Guerin (BCG) (re)vaccination in coronavirus disease 2019 (COVID-19) morbidity and mortality in frontline health care workers. The workflow was designed to review data iteratively during the trial, compile frequent reports to the DSMB, and prepare for rapid pharmacometric analysis. Clinical trial datasets (n = 41) were transferred iteratively throughout the trial for review. An RMarkdown based pharmacometric processing script was written to automatically generate reports for evaluation by the DSMB. Reports were compiled, reviewed, and sent to the DSMB on average three days after the data cut-off, reflecting the trial progress in real-time. The script was also utilized to prepare for the trial pharmacometric analyses. The same source data was used to create analysis datasets in NONMEM format and to support model script development. The primary endpoint analysis was completed three days after data lock and unblinding, and the secondary endpoint analyses two weeks later. The constructive collaboration between clinical, data management, and pharmacometric teams enabled this efficient, timely, and reproducible pharmacometrics workflow.

G protein-coupled receptors are important drug targets that engage and activate signaling transducers in multiple cellular compartments. Delineating therapeutic signaling from signaling associated with adverse events is an important step towards rational drug design. The glucagon-like peptide-1 receptor (GLP-1R) is a validated target for the treatment of diabetes and obesity, but drugs that target this receptor are a frequent cause of adverse events. Using recently developed biosensors, we explored the ability of GLP-1R to activate 15 pathways in 4 cellular compartments and demonstrate that modifications aimed at improving the therapeutic potential of GLP-1R agonists greatly influence compound efficacy, potency, and safety in a pathway- and compartment-selective manner. These findings, together with comparative structure analysis, time-lapse microscopy, and phosphoproteomics, reveal unique signaling signatures for GLP-1R agonists at the level of receptor conformation, functional selectivity, and location bias, thus associating signaling neighborhoods with functionally distinct cellular outcomes and clinical consequences. Agonists of the glucagon-like peptide-1 receptor are used to treat diabetes and obesity. Here, Wright et al. investigate the subcellular location of the receptor's signaling events and uncover associations between signaling profiles and adverse drug reactions.

Drug-eluting beads made of responsive polyelectrolyte networks are used in the treatment of liver cancer. Aggregates of loaded drugs in complex with the networks dissolve upon release, causing swelling of the network. According to a recent mechanism the release and swelling rates are controlled by the mass transport of drug through a depletion layer created in the microgel. We hypothesise that the mechanism, in which the stability of the drug aggregates and the swelling properties of the network play crucial roles, offers means to control the release profile also for other drugs. To test this, we investigated the loading and release properties of amitriptyline, chlorpromazine and doxepin in polyacrylate, hyaluronate and DCbeadTM microgels in a microfluidic setup. Loaded drugs could be released to a medium with physiological ionic strength and pH. The binding strength increased with decreasing critical micelle concentration of the drugs and increasing linear charge density of network chains. Microgels displayed drug-rich core/swollen shell coexistence, and swelled during release at a rate in agreement with the depletion layer mechanism, indicating its generality. The results demonstrate the potential of microgels as vehicles for amphiphilic drugs and the usefulness of the microfluidics method for in vitro studies of such systems.

A different drug–drug interaction (DDI) scenario may exist in patients with chronic kidney disease (CKD) compared with healthy volunteers (HVs), depending on the interplay between drug–drug and disease (drug-drug-disease interaction (DDDI)). Physiologically-based pharmacokinetic (PBPK) modeling, in lieu of a clinical trial, is a promising tool for evaluating these complex DDDIs in patients. However, the prediction confidence of PBPK modeling in the severe CKD population is still low when nonrenal pathways are involved. More mechanistic virtual disease population and robust validation cases are needed. To this end, we aimed to: (i) understand the implications of severe CKD on statins (atorvastatin, simvastatin, and rosuvastatin) pharmacokinetics (PK) and DDI; and (ii) predict untested clinical scenarios of statin-roxadustat DDI risks in patients to guide suitable dose regimens. A novel virtual severe CKD population was developed incorporating the disease effect on both renal and nonrenal pathways. Drug and disease PBPK models underwent a four-way validation. The verified PBPK models successfully predicted the altered PKs in patients for substrates and inhibitors and recovered the observed statin-rifampicin DDIs in patients and the statin-roxadustat DDIs in HVs within 1.25- and 2-fold error. Further sensitivity analysis revealed that the severe CKD effect on statins PK is mainly mediated by hepatic BCRP for rosuvastatin and OATP1B1/3 for atorvastatin. The magnitude of statin-roxadustat DDI in patients with severe CKD was predicted to be similar to that in HVs. PBPK-guided suitable dose regimens were identified to minimize the risk of side effects or therapeutic failure of statins when co-administered with roxadustat.

Antibiotic resistance is becoming an accelerating issue, both in the treatment of nosocomial infections and for public health in general. There is a pressing need for innovative methods to understand how available antibiotics could be combined to overcome and limit the emergence of resistance. Pharmacokinetic-pharmacodynamic (PKPD) modelling and simulation (M&S) is a promising tool in quantitatively characterising longitudinal antibiotic drug effects, especially in exploring antibiotic combinations where the concentration ratios of the component drugs, their interactions and relative contributions to the combined drug effects change over time. This thesis aimed to develop approaches to quantify the effects of antibiotic combinations on gram-negative bacteria by PKPD M&S. Pre-clinical data for antibiotics alone and in combination, including in vitro static and dynamic time-kill data and in vivo murine thigh infection data, were quantitatively described by semi-mechanistic PKPD models, as exemplified in the thesis by the combination of colistin and ciprofloxacin against four Escherichia coli strains and by the combination of polymyxin B and minocycline against two Klebsiella pneumoniae strains. The developed PKPD models showed good translational ability, as demonstrated by bridging across strains, predicting in vitro drug effects under dynamic settings, integrating all available in vitro and in vivo data, and quantifying the discrepancy between the in vitro and in vivo settings. These developed models were used to explore the therapeutic potential of the combinations. The value of adding low dosages of colistin (thus low toxicity) to ciprofloxacin treatment for E. coli strains with decreased susceptibility, and of combining polymyxin B and minocycline against K. pneumoniae when either drug alone has a limited antibacterial effect, was shown. In addition, a methodological framework was developed to quantify antibiotic effects from time-lapse microscopic images

Aims Low-dose rivaroxaban has been indicated for the management of atherosclerotic cardiovascular disease (ASCVD) after recent (2019-2020) updates to European guidelines. We aimed to describe prescription trends of low-dose rivaroxaban in ASCVD patients over the period 2015-2022 in two European countries, to compare the trends before and after guideline changes, and to determine the characteristics of users. Methods In a cross-sectional interrupted time series analysis, utilization of low-dose rivaroxaban (2.5 mg, twice daily) was measured in Clinical Practice Research Datalink Aurum (United Kingdom [UK]) and the PHARMO Database Network (the Netherlands) from 1 January 2015 to 28 February 2022 in patients with an ASCVD diagnosis. Incidence rates (IRs) and incidence rate ratios (IRRs) of new use (within 182 days) compared to the reference period, 2015-2018, were calculated. Age, sex and comorbidities of users were compared to those of nonusers. Results In the UK, from 721 271 eligible subjects the IR of new use of low-dose rivaroxaban in the period 2015-2018, before guideline changes, was 12.4 per 100 000 person-years and after guideline changes in 2020-2022 was 124.0 (IRR 10.0, 95% confidence interval [CI] 8.5, 11.8). In the Netherlands from 394 851 subjects, the IR in 2015-2018 was 2.4 per 100 000 person-years and in 2020 was 16.3 (IRR 6.7, 95% CI 4.0, 11.4). Users were younger (UK mean difference [MD] −6.1 years, Netherlands −2.4 years; P < .05) and more likely to be male (UK difference 11.5%, Netherlands 13.4%; P < .001) than nonusers. Conclusions There was a statistically significant increase in the use of low-dose rivaroxaban for the management of ASCVD after guideline changes in the UK and the Netherlands. There were international differences, but low-dose rivaroxaban has not been put into widespread practice.

Drug permeation across the intestinal epithelium is a prerequisite for successful oral drug delivery. The increased interest in oral administration of peptides, as well as poorly soluble and poorly permeable compounds such as drugs for targeted protein degradation, have made permeability a key parameter in oral drug product development. This review describes the various in vitro, in silico and in vivo methodologies that are applied to determine drug permeability in the human gastrointestinal tract and identifies how they are applied in the different stages of drug development. The various methods used to predict, estimate or measure permeability values, ranging from in silico and in vitro methods all the way to studies in animals and humans, are discussed with regard to their advantages, limitations and applications. A special focus is put on novel techniques such as computational approaches, gut-on-chip models and human tissue-based models, where significant progress has been made in the last few years. In addition, the impact of permeability estimations on PK predictions in PBPK modeling, the degree to which excipients can affect drug permeability in clinical studies and the requirements for colonic drug absorption are addressed.

Intranasal administration is a drug delivery approach to provide a non-invasive pharmacological response in the central nervous system with relatively small peripheral side effects. To improve the residence time of intranasal drug delivery systems in the nasal mucosa, mucoadhesive polymers (e.g., chitosan) can be used. Here, insulin-loaded chitosan nanoparticles were synthesized and their physiochemical properties were evaluated based on requirements of intranasal administration. The nanoparticles were spherical (a hydrodynamic diameter of 165.3 nm, polydispersity index of 0.24, and zeta potential of +21.6 mV) that granted mucoadhesion without any noticeable toxicity to the nasal tissue. We applied a new approach using the Krebs-Henseleit buffer solution along with simulated nasal fluid in a Franz's diffusion cell to study this intranasal drug delivery system. We used the Krebs-Henseleit buffer because of its ability to supply glucose to the cells which serves as a novel ex vivo diffusion medium to maintain the viability of the tissue during the experiment. Based on diffusion rate and histopathological endpoints, the Krebs-Henseleit buffer solution can be a substituent solution to the commonly used simulated nasal fluid for such drug delivery systems.

Polymers are a group of macromolecules used in formulations of pharmaceuticals, one example being the delivery system DC Bead™. Further, some of the most abundant and for drug delivery important constituents of the subcutaneous tissue are charged polymers (polyelectrolytes), e.g. collagen, hyaluronic acid, and chondroitin sulfate. The interactions between these subcutaneous polyelectrolytes and drug molecules are believed to heavily affect the transport and absorption of subcutaneously injected drugs. To increase the understanding of how the interactions between subcutaneous polymers and drug molecules affect the pharmaceutical behavior in subcutaneous tissue, we developed a new microfluidic-based platform. The platform is used to study interactions between polyelectrolytes and drug molecules, and can beyond the investigation of subcutaneous interactions be used to develop polyelectrolyte-based microgel formulations. In this thesis, the microfluidic method denoted “Microfluidic chip for interactions studies” (MIS) is presented, and the design, validation, and several examples of usage are described. The method which is based on microfluidic instrumentation, utilizes spherical microgels created using different types of polymers/polyelectrolytes. These hydrogels collapse when experiencing attractive interactions with drug molecules making it possible to investigate drug binding by studying the volume change of the microgels. We prove that the interactions are strongly affected by charges both on the gel networks and the drug molecules. Further, the aggregation behavior of drugs in a polyelectrolyte-rich environment is studied in detail. Results show that both a strong aggregation behavior and a high charge on the drugs may affect the transport through a network of polyelectrolytes. The behavior of drugs in subcutaneous polyelectrolyte-rich environments such as hyaluronic acid networks, can partly explain bioavailability and absorption rates of the drugs in vivo. Sever

Background and PurposeCannabidiol (CBD) is used clinically as an anticonvulsant. Its precise mechanism of action has remained unclear. CBD was recently demonstrated to enhance the activity of the neuronal K(V)7.2/7.3 channel, which may be one important contributor to CBD anticonvulsant effect. Curiously, CBD inhibits the closely related cardiac K(V)7.1/KCNE1 channel. Whether and how CBD affects other K(V)7 subtypes remains uninvestigated and the CBD interaction sites mediating these diverse effects remain unknown. Experimental ApproachHere, we used electrophysiology, molecular dynamics simulations, molecular docking and site-directed mutagenesis to address these questions. Key ResultsWe found that CBD modulates the activity of all human K(V)7 subtypes and that the effects are subtype dependent. CBD enhanced the activity of K(V)7.2-7.5 subtypes, seen as a V-50 shift towards more negative voltages or increased maximum conductance. In contrast, CBD inhibited the K(V)7.1 and K(V)7.1/KCNE1 channels, seen as a V-50 shift towards more positive voltages and reduced conductance. In K(V)7.2 and K(V)7.4, we propose a CBD interaction site at the subunit interface in the pore domain that overlaps with the interaction site of other compounds, notably the anticonvulsant retigabine. However, CBD relies on other residues for its effects than the conserved tryptophan that is critical for retigabine effects. We propose a similar, though not identical CBD site in K(V)7.1, with a non-conserved phenylalanine being important. Conclusions and ImplicationsWe identify novel targets of CBD, contributing to a better understanding of CBD clinical effects and provide mechanistic insights into how CBD modulates different K(V)7 subtypes., Funding Agencies|Goeran Gustafsson Foundation; European Research Council (ERC) [850622]; Swedish Research Council [VR2018-04905, VR2021-01885]; Science for Life Laboratory; Marie-Sklodowska Curie Fellowship Lipopeutics [898762]

The usage of nanomaterials for rheumatoid arthritis (RA) treatment can improve bioavailability and enable selective targeting. The current study prepares and evaluates the in vivo biological effects of a novel hydroxyapatite/vitamin B-12 nanoformula in Complete Freunds adjuvant-induced arthritis in rats. The synthesized nanoformula was characterized using XRD, FTIR, BET analysis, HERTEM, SEM, particle size, and zeta potential. We synthesized pure HAP NPs with 71.01% loading weight percentages of Vit B12 and 49 mg/g loading capacity. Loading of vitamin B-12 on hydroxyapatite was modeled by Monte Carlo simulation. Anti-arthritic, anti-inflammatory, and antioxidant effects of the prepared nanoformula were assessed. Treated arthritic rats showed lower levels of RF and CRP, IL-1 beta, TNF-alpha, IL-17, and ADAMTS-5, but higher IL-4 and TIMP-3 levels. In addition, the prepared nanoformula increased GSH content and GST antioxidant activity while decreasing LPO levels. Furthermore, it reduced the expression of TGF-beta mRNA. Histopathological examinations revealed an improvement in joint injuries through the reduction of inflammatory cell infiltration, cartilage deterioration, and bone damage caused by Complete Freunds adjuvant. These findings indicate that the anti-arthritic, antioxidant, and anti-inflammatory properties of the prepared nanoformula could be useful for the development of new anti-arthritic treatments., Funding Agencies|King Salman Center for Disability Research [KSRG-2022-047]

Many new psychoactive substances (NPS) are stimulants, and information about their potency and abuse potential is often lacking. To start addressing this need, a method measuring the inhibition of the dopamine, serotonin, and norepinephrine transporters (DAT, SERT, and NET) by stimulant drugs was developed. The use of a proprietary fluorescent dye mixture and three cell lines (CHO-K1, HEK 293, and MDCK), each expressing a single transporter, allowed for a semiautomated, one-pot determination of inhibition in a 384-well format. The method was validated using well characterized stimulants, including cocaine, amphetamine, 3,4-methylenedioxymethamphetamine (MDMA), & alpha;-PVP, and fluoxetine and performed similarly to other methods. Seven synthetic cathinones all showed highest potency for DAT inhibition, followed by NET and SERT. The rank potency for DAT inhibition IC50 (nM) was MPHP (4.53) > 4Cl-& alpha;-PVP (8.05) > 3F-& alpha;-PVP (12.7) > & alpha;-PiHP (13.4) > N-ethylpentylone (16.9) > N-ethylhexedrone (44.5) > 4-methylpentedrone (261). All but 4-methylpentedrone were more potent than amphetamine (257) and cocaine (111). The DAT/SERT inhibition ratio for the cathinones was in the range from 5.02 for 4-methylpentedrone to >3730 for & alpha;-PiHP, compared to 1.64 for cocaine and >4030 for & alpha;-PVP. All seven substances had inhibition profiles similar to those of potent stimulants with high abuse potential., Funding Agencies|Public Health Agency of Sweden; National Board of Forensic Medicine

Drug permeation across the intestinal epithelium is a prerequisite for successful oral drug delivery. The increased interest in oral administration of peptides, as well as poorly soluble and poorly permeable compounds such as drugs for targeted protein degradation, have made permeability a key parameter in oral drug product development. This review describes the various in vitro, in silico and in vivo methodologies that are applied to determine drug permeability in the human gastrointestinal tract and identifies how they are applied in the different stages of drug development. The various methods used to predict, estimate or measure permeability values, ranging from in silico and in vitro methods all the way to studies in animals and humans, are discussed with regard to their advantages, limitations and applications. A special focus is put on novel techniques such as computational approaches, gut-on-chip models and human tissue-based models, where significant progress has been made in the last few years. In addition, the impact of permeability estimations on PK predictions in PBPK modeling, the degree to which excipients can affect drug permeability in clinical studies and the requirements for colonic drug absorption are addressed.

Selective androgen receptor modulators (SARMs) such as ACP-105 are prohibited in sports due to their anabolic properties. ACP-105 has in previous equine studies shown to undergo extensive metabolism, which makes its metabolite profile important to investigate in humans, since the metabolism is unknown in this species. The aims of the study were to systematically optimize in vitro microsome incubations for improved metabolite yield and to utilize a multivariate data analysis (MVDA) approach to aid the metabolite discovery. Microsomes together with S9 fractions were used at optimal conditions, both with and without phase II additives. Furthermore, the relevance of the in vitro derived metabolites was evaluated as analytical targets in doping control by comparison with results from a human post-administration urine sample collected after a single dose of 100 µg ACP-105. All samples were analyzed with liquid chromatography - Orbitrap mass spectrometry. The use of the systematical optimization and MVDA greatly simplified the search and a total of 18 in vitro metabolites were tentatively identified. The yield of the two main monohydroxylated isomers increased by 24 and 10 times, respectively. In the human urine sample, a total of seven metabolites of ACP-105, formed by a combination of hydroxylations and glucuronic acid conjugations, were tentatively identified. The main metabolites were two monohydroxylated forms that are suggested as analytical targets for human doping control after hydrolysis. All the in vivo metabolites could be detected with the MVDA approach on the in vitro models, demonstrating its usefulness for prediction of the in vivo metabolite profile.

Therapeutic dose of a medication refers to the quantity of a drug required to produce a pharmacological effect without causing unacceptable adverse events. Dose selection in the clinical setting is not straight forward due to various factors, including specific patient factors such as age, sex, weight, genetic variants and renal/hepatic function, as well as external factors such as food and co-medication, all of which can influence the efficacy and safety of a drug. In silico mechanistic mathematical models, such as physiologically-based pharmacokinetic (PBPK) models, can aid dose setting by considering all these factors into a mechanistic model. The aim of this thesis was to enhance the application of PBPK modelling in drug development, with specific focus on drug-drug interactions and evaluations of genotype and disease dependencies. Additionally, the thesis assessed the implications of pre-defined PBPK platform differences. PBPK modelling, in conjunction with in vitro generated data, quantitative proteomics, pharmacodynamic models, and/or chronic kidney disease (CKD) population models, were applied to predict drug interactions in various clinically relevant cases. The model drugs used were statins, itraconazole and roxadustat. Additionally, two different PBPK platforms, Simcyp and PK-Sim, were employed throughout the different projects of this thesis providing insights on platform differences and its potential implications. The generated in vitro data and developed PBPK models provided novel insights into: 1) the relative contribution of itraconazole metabolites to the clinical drug-drug interactions, 2) the impact of statins’ distribution to muscle by transporters, 3) the critical pathways involved in the effects of disease on statins and roxadustat pharmacokinetics and drug interactions, 4) the influence of disease not only on statins’ pharmacokinetics but also on their efficacy and safety, 5) differences in software and potential implications when selecting

Large clinical trials often generate complex and large datasets which need to be presented frequently throughout the trial for interim analysis or to inform a data safety monitory board (DSMB). In addition, reliable and traceability are required to ensure reproducibility in pharmacometric data analysis. A reproducible pharmacometric analysis workflow was developed during a large clinical trial involving 1000 participants over one year testing Bacillus Calmette-Guerin (BCG) (re)vaccination in coronavirus disease 2019 (COVID-19) morbidity and mortality in frontline health care workers. The workflow was designed to review data iteratively during the trial, compile frequent reports to the DSMB, and prepare for rapid pharmacometric analysis. Clinical trial datasets (n = 41) were transferred iteratively throughout the trial for review. An RMarkdown based pharmacometric processing script was written to automatically generate reports for evaluation by the DSMB. Reports were compiled, reviewed, and sent to the DSMB on average three days after the data cut-off, reflecting the trial progress in real-time. The script was also utilized to prepare for the trial pharmacometric analyses. The same source data was used to create analysis datasets in NONMEM format and to support model script development. The primary endpoint analysis was completed three days after data lock and unblinding, and the secondary endpoint analyses two weeks later. The constructive collaboration between clinical, data management, and pharmacometric teams enabled this efficient, timely, and reproducible pharmacometrics workflow.

Protein drugs, such as monoclonal antibodies, have proved successful in treating cancer and immune system diseases. The structural complexity of these molecules requires careful handling to ensure integrity and stability of the drug. In this study, a failure mode and effects analysis was performed based on a Gemba Walk method in a Swedish University Hospital. The Gemba Walk is focused on pharmacists observing the actual supply process steps from distributor, pharmacy cleanroom to patient administration. Relevant protein drugs are chosen based on sales statistics within the hospital and the corresponding wards were observed. Further is the Double Diamond design method used to identify major risks and deliver mitigation strategies. The study identified potential stress factors such as temperature, shock by impact, shaking, vibration and light exposure. There were also risks associated with porters' and healthcare professionals' lack of awareness and access to information. These risk factors may cause loss of efficacy and quality of the protein drug, potentially leading to patient safety concerns. In this study, a simulation is also performed to list measures that theoretically should be in place to ensure the quality of the protein drug, for example validated and protocol-based compounding in cleanroom, training and validated transports., RealHOPE

A different drug–drug interaction (DDI) scenario may exist in patients with chronic kidney disease (CKD) compared with healthy volunteers (HVs), depending on the interplay between drug–drug and disease (drug-drug-disease interaction (DDDI)). Physiologically-based pharmacokinetic (PBPK) modeling, in lieu of a clinical trial, is a promising tool for evaluating these complex DDDIs in patients. However, the prediction confidence of PBPK modeling in the severe CKD population is still low when nonrenal pathways are involved. More mechanistic virtual disease population and robust validation cases are needed. To this end, we aimed to: (i) understand the implications of severe CKD on statins (atorvastatin, simvastatin, and rosuvastatin) pharmacokinetics (PK) and DDI; and (ii) predict untested clinical scenarios of statin-roxadustat DDI risks in patients to guide suitable dose regimens. A novel virtual severe CKD population was developed incorporating the disease effect on both renal and nonrenal pathways. Drug and disease PBPK models underwent a four-way validation. The verified PBPK models successfully predicted the altered PKs in patients for substrates and inhibitors and recovered the observed statin-rifampicin DDIs in patients and the statin-roxadustat DDIs in HVs within 1.25- and 2-fold error. Further sensitivity analysis revealed that the severe CKD effect on statins PK is mainly mediated by hepatic BCRP for rosuvastatin and OATP1B1/3 for atorvastatin. The magnitude of statin-roxadustat DDI in patients with severe CKD was predicted to be similar to that in HVs. PBPK-guided suitable dose regimens were identified to minimize the risk of side effects or therapeutic failure of statins when co-administered with roxadustat.

G protein-coupled receptors are important drug targets that engage and activate signaling transducers in multiple cellular compartments. Delineating therapeutic signaling from signaling associated with adverse events is an important step towards rational drug design. The glucagon-like peptide-1 receptor (GLP-1R) is a validated target for the treatment of diabetes and obesity, but drugs that target this receptor are a frequent cause of adverse events. Using recently developed biosensors, we explored the ability of GLP-1R to activate 15 pathways in 4 cellular compartments and demonstrate that modifications aimed at improving the therapeutic potential of GLP-1R agonists greatly influence compound efficacy, potency, and safety in a pathway- and compartment-selective manner. These findings, together with comparative structure analysis, time-lapse microscopy, and phosphoproteomics, reveal unique signaling signatures for GLP-1R agonists at the level of receptor conformation, functional selectivity, and location bias, thus associating signaling neighborhoods with functionally distinct cellular outcomes and clinical consequences. Agonists of the glucagon-like peptide-1 receptor are used to treat diabetes and obesity. Here, Wright et al. investigate the subcellular location of the receptor's signaling events and uncover associations between signaling profiles and adverse drug reactions.

Ibrutinib is a Bruton tyrosine kinase (Btk) inhibitor for treating chronic lymphocytic leukemia (CLL). It has also been associated with hypertension. The optimal dosing schedule for mitigating this adverse effect is currently under discussion. A quantification of relationships between systemic ibrutinib exposure and efficacy (i.e., leukocyte count and sum of the product of perpendicular diameters [SPD] of lymph nodes) and hypertension toxicity (i.e., blood pressure), and their association with overall survival is needed. Here, we present a semi-mechanistic pharmacokinetic-pharmacodynamic modeling framework to characterize such relationships and facilitate dose optimization. Data from a phase Ib/II study were used, including ibrutinib plasma concentrations to derive daily 0-24-h area under the concentration-time curve, leukocyte count, SPD, survival, and blood pressure measurements. A nonlinear mixed effects modeling approach was applied, considering ibrutinib's pharmacological action and CLL cell dynamics. The final framework included (i) an integrated model for SPD and leukocytes consisting of four CLL cell subpopulations with ibrutinib inhibiting phosphorylated Btk production, ( ii) a turnover model in which ibrutinib stimulates an increase in blood pressure, and (iii) a competing risk model for dropout and death. Simulations predicted that the approved dosing schedule had a slightly higher efficacy (24-month, progression- free survival [PFS] 98%) than de-escalation schedules (24-month, average PFS approximate to 97%); the latter had, on average, approximate to 20% lower proportions of patients with hypertension. The developed modeling framework offers an improved understanding of the relationships among ibrutinib exposure, efficacy and toxicity biomarkers. This framework can serve as a platform to assess dosing schedules in a biologically plausible manner.

Drug-eluting beads made of responsive polyelectrolyte networks are used in the treatment of liver cancer. Aggregates of loaded drugs in complex with the networks dissolve upon release, causing swelling of the network. According to a recent mechanism the release and swelling rates are controlled by the mass transport of drug through a depletion layer created in the microgel. We hypothesise that the mechanism, in which the stability of the drug aggregates and the swelling properties of the network play crucial roles, offers means to control the release profile also for other drugs. To test this, we investigated the loading and release properties of amitriptyline, chlorpromazine and doxepin in polyacrylate, hyaluronate and DCbeadTM microgels in a microfluidic setup. Loaded drugs could be released to a medium with physiological ionic strength and pH. The binding strength increased with decreasing critical micelle concentration of the drugs and increasing linear charge density of network chains. Microgels displayed drug-rich core/swollen shell coexistence, and swelled during release at a rate in agreement with the depletion layer mechanism, indicating its generality. The results demonstrate the potential of microgels as vehicles for amphiphilic drugs and the usefulness of the microfluidics method for in vitro studies of such systems.

Drug permeation across the intestinal epithelium is a prerequisite for successful oral drug delivery. The increased interest in oral administration of peptides, as well as poorly soluble and poorly permeable compounds such as drugs for targeted protein degradation, have made permeability a key parameter in oral drug product development. This review describes the various in vitro, in silico and in vivo methodologies that are applied to determine drug permeability in the human gastrointestinal tract and identifies how they are applied in the different stages of drug development. The various methods used to predict, estimate or measure permeability values, ranging from in silico and in vitro methods all the way to studies in animals and humans, are discussed with regard to their advantages, limitations and applications. A special focus is put on novel techniques such as computational approaches, gut-on-chip models and human tissue-based models, where significant progress has been made in the last few years. In addition, the impact of permeability estimations on PK predictions in PBPK modeling, the degree to which excipients can affect drug permeability in clinical studies and the requirements for colonic drug absorption are addressed.

Antibiotic resistance is becoming an accelerating issue, both in the treatment of nosocomial infections and for public health in general. There is a pressing need for innovative methods to understand how available antibiotics could be combined to overcome and limit the emergence of resistance. Pharmacokinetic-pharmacodynamic (PKPD) modelling and simulation (M&S) is a promising tool in quantitatively characterising longitudinal antibiotic drug effects, especially in exploring antibiotic combinations where the concentration ratios of the component drugs, their interactions and relative contributions to the combined drug effects change over time. This thesis aimed to develop approaches to quantify the effects of antibiotic combinations on gram-negative bacteria by PKPD M&S. Pre-clinical data for antibiotics alone and in combination, including in vitro static and dynamic time-kill data and in vivo murine thigh infection data, were quantitatively described by semi-mechanistic PKPD models, as exemplified in the thesis by the combination of colistin and ciprofloxacin against four Escherichia coli strains and by the combination of polymyxin B and minocycline against two Klebsiella pneumoniae strains. The developed PKPD models showed good translational ability, as demonstrated by bridging across strains, predicting in vitro drug effects under dynamic settings, integrating all available in vitro and in vivo data, and quantifying the discrepancy between the in vitro and in vivo settings. These developed models were used to explore the therapeutic potential of the combinations. The value of adding low dosages of colistin (thus low toxicity) to ciprofloxacin treatment for E. coli strains with decreased susceptibility, and of combining polymyxin B and minocycline against K. pneumoniae when either drug alone has a limited antibacterial effect, was shown. In addition, a methodological framework was developed to quantify antibiotic effects from time-lapse microscopic images

Aims Low-dose rivaroxaban has been indicated for the management of atherosclerotic cardiovascular disease (ASCVD) after recent (2019-2020) updates to European guidelines. We aimed to describe prescription trends of low-dose rivaroxaban in ASCVD patients over the period 2015-2022 in two European countries, to compare the trends before and after guideline changes, and to determine the characteristics of users. Methods In a cross-sectional interrupted time series analysis, utilization of low-dose rivaroxaban (2.5 mg, twice daily) was measured in Clinical Practice Research Datalink Aurum (United Kingdom [UK]) and the PHARMO Database Network (the Netherlands) from 1 January 2015 to 28 February 2022 in patients with an ASCVD diagnosis. Incidence rates (IRs) and incidence rate ratios (IRRs) of new use (within 182 days) compared to the reference period, 2015-2018, were calculated. Age, sex and comorbidities of users were compared to those of nonusers. Results In the UK, from 721 271 eligible subjects the IR of new use of low-dose rivaroxaban in the period 2015-2018, before guideline changes, was 12.4 per 100 000 person-years and after guideline changes in 2020-2022 was 124.0 (IRR 10.0, 95% confidence interval [CI] 8.5, 11.8). In the Netherlands from 394 851 subjects, the IR in 2015-2018 was 2.4 per 100 000 person-years and in 2020 was 16.3 (IRR 6.7, 95% CI 4.0, 11.4). Users were younger (UK mean difference [MD] −6.1 years, Netherlands −2.4 years; P < .05) and more likely to be male (UK difference 11.5%, Netherlands 13.4%; P < .001) than nonusers. Conclusions There was a statistically significant increase in the use of low-dose rivaroxaban for the management of ASCVD after guideline changes in the UK and the Netherlands. There were international differences, but low-dose rivaroxaban has not been put into widespread practice.

Intranasal administration is a drug delivery approach to provide a non-invasive pharmacological response in the central nervous system with relatively small peripheral side effects. To improve the residence time of intranasal drug delivery systems in the nasal mucosa, mucoadhesive polymers (e.g., chitosan) can be used. Here, insulin-loaded chitosan nanoparticles were synthesized and their physiochemical properties were evaluated based on requirements of intranasal administration. The nanoparticles were spherical (a hydrodynamic diameter of 165.3 nm, polydispersity index of 0.24, and zeta potential of +21.6 mV) that granted mucoadhesion without any noticeable toxicity to the nasal tissue. We applied a new approach using the Krebs-Henseleit buffer solution along with simulated nasal fluid in a Franz's diffusion cell to study this intranasal drug delivery system. We used the Krebs-Henseleit buffer because of its ability to supply glucose to the cells which serves as a novel ex vivo diffusion medium to maintain the viability of the tissue during the experiment. Based on diffusion rate and histopathological endpoints, the Krebs-Henseleit buffer solution can be a substituent solution to the commonly used simulated nasal fluid for such drug delivery systems.

Polymers are a group of macromolecules used in formulations of pharmaceuticals, one example being the delivery system DC Bead™. Further, some of the most abundant and for drug delivery important constituents of the subcutaneous tissue are charged polymers (polyelectrolytes), e.g. collagen, hyaluronic acid, and chondroitin sulfate. The interactions between these subcutaneous polyelectrolytes and drug molecules are believed to heavily affect the transport and absorption of subcutaneously injected drugs. To increase the understanding of how the interactions between subcutaneous polymers and drug molecules affect the pharmaceutical behavior in subcutaneous tissue, we developed a new microfluidic-based platform. The platform is used to study interactions between polyelectrolytes and drug molecules, and can beyond the investigation of subcutaneous interactions be used to develop polyelectrolyte-based microgel formulations. In this thesis, the microfluidic method denoted “Microfluidic chip for interactions studies” (MIS) is presented, and the design, validation, and several examples of usage are described. The method which is based on microfluidic instrumentation, utilizes spherical microgels created using different types of polymers/polyelectrolytes. These hydrogels collapse when experiencing attractive interactions with drug molecules making it possible to investigate drug binding by studying the volume change of the microgels. We prove that the interactions are strongly affected by charges both on the gel networks and the drug molecules. Further, the aggregation behavior of drugs in a polyelectrolyte-rich environment is studied in detail. Results show that both a strong aggregation behavior and a high charge on the drugs may affect the transport through a network of polyelectrolytes. The behavior of drugs in subcutaneous polyelectrolyte-rich environments such as hyaluronic acid networks, can partly explain bioavailability and absorption rates of the drugs in vivo. Sever

Background and Objective As a result of changes in physiology during pregnancy, the pharmacokinetics (PK) of drugs can be altered. It is unclear whether under- or overexposure occurs in pregnant cancer patients and thus also whether adjustments in dosing regimens are required. Given the severity of the malignant disease and the potentially high impact on both the mother and child, there is a high unmet medical need for adequate and tolerable treatment of this patient population. We aimed to develop and evaluate a semi-physiological enriched model that incorporates physiological changes during pregnancy into available population PK models developed from non-pregnant patient data. Methods Gestational changes in plasma protein levels, renal function, hepatic function, plasma volume, extracellular water and total body water were implemented in existing empirical PK models for docetaxel, paclitaxel, epirubicin and doxorubicin. These models were used to predict PK profiles for pregnant patients, which were compared with observed data obtained from pregnant patients. Results The observed PK profiles were well described by the model. For docetaxel, paclitaxel and doxorubicin, an overprediction of the lower concentrations was observed, most likely as a result of a lack of data on the gestational changes in metabolizing enzymes. For paclitaxel, epirubicin and doxorubicin, the semi-physiological enriched model performed better in predicting PK in pregnant patients compared with a model that was not adjusted for pregnancy-induced changes. Conclusion By incorporating gestational changes into existing population pharmacokinetic models, it is possible to adequately predict plasma concentrations of drugs in pregnant patients which may inform dose adjustments in this population.