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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

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.

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.

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.

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.

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.

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.

Gliclazide was approved as a treatment for type 2 diabetes in an era before model-based drug development, and consequently, the recommended doses were not optimised with modern methods. To investigate various dosing regimens of gliclazide, we used publicly available data to characterise the dose-response relationship using pharmacometric models. A literature search identified 21 published gliclazide pharmacokinetic (PK) studies with full profiles. These were digitised, and a PK model was developed for immediate- (IR) and modified-release (MR) formulations. Data from a gliclazide dose-ranging study of postprandial glucose were used to characterise the concentration-response relationship using the integrated glucose-insulin model. Simulations from the full model showed that the maximum effect was 44% of the patients achieving HbA1c <7%, with 11% experiencing glucose <3 mmol/L and the most sensitive patients (i.e., 5% most extreme) experiencing 35 min of hypoglycaemia. Simulations revealed that the recommended IR dose (320 mg) was appropriate with no efficacy gain with increased dose. However, the recommended dose for the MR formulation may be increased to 270 mg, with more patients achieving HbA1c goals (i.e., HbA1c <7%) without a hypoglycaemic risk higher than the resulting risk from the recommended IR dose.

This study aimed to evaluate a material sparing method to predict tabletability and compactibility relationships. Seven a-lactose monohydrate powders with varying particle size were used as test materials. The compressibility of the powders was determined experimentally, whereas tabletability and compactibility profiles were derived both experimentally and predicted. In the prediction method, two experimental compression parameters (Kawakita b-1 and Heckel plastic stiffness) and a single tensile strength reference value were used, all necessary data obtained from a single compression experiment. For both predicted and experimental relationships, compaction and tableting parameters (performance indicators) were calculated. The correction for viscoelastic recovery was successful in generating compressibility profiles that corresponded to the series of experimental out-of-die tablet porosities. For both the tabletability and compactibility, the experimental and predicted profiles showed a high degree of similarity. Good correlations were obtained between the predicted and experimental compaction and tableting parameters. It is concluded that the hybrid prediction method is a material sparing method, which can give good approximations of tabletability and compactibility relationships. The prediction method has the potential to be included as a part of a protocol for the characterisation of the tableting performance of particulate solids.

Background: Linezolid is an effective, but toxic anti-tuberculosis drug that is currently recommended for the treatment of drug-resistant tuberculosis. Improved oxazolidinones should have a better safety profile, while preserving efficacy. Delpazolid is a novel oxazolidinone developed by LegoChem Biosciences Inc. that has been evaluated up to phase 2a clinical trials. Since oxazolidinone toxicity can occur late in treatment, LegoChem Biosciences Inc. and the PanACEA Consortium designed DECODE to be an innovative dose-ranging study with long-term follow-up for determining the exposure-response and exposure-toxicity relationship of delpazolid to support dose selection for later studies. Delpazolid is administered in combination with bedaquiline, delamanid and moxifloxacin. Methods: Seventy-five participants with drug-sensitive, pulmonary tuberculosis will receive bedaquiline, delamanid and moxifloxacin, and will be randomized to delpazolid dosages of 0 mg, 400 mg, 800 mg, 1200 mg once daily, or 800 mg twice daily, for 16 weeks. The primary efficacy endpoint will be the rate of decline of bacterial load on treatment, measured by MGIT liquid culture time to detection from weekly sputum cultures. The primary safety endpoint will be the proportion of oxazolidinone class toxicities; neuropathy, myelosuppression, or tyramine pressor response. Participants who convert to negative liquid media culture by week 8 will stop treatment after the end of their 16-week course and will be observed for relapse until week 52. Participants who do not convert to negative culture will receive continuation phase treatment with rifampicin and isoniazid to complete a six-month treatment course. Discussion: DECODE is an innovative dose-finding trial, designed to support exposure-response modelling for safe and effective dose selection. The trial design allows assessment of occurrence of late toxicities as observed with linezolid, which is necessary in clinical evaluation of novel oxazolidinone

The blood-brain barrier (BBB) poses major challenges to drug delivery to the CNS. SFTI-1 and kalata B1 are cyclic cell-penetrating peptides (cCPPs) with high potential to be used as scaffolds for drug delivery. We here studied their transport across the BBB and distribution within the brain to gauge the potential of these two cCPPs as scaffolds for CNS drugs. In a rat model, SFTI-1 exhibited, for a peptide, high extent of BBB transport with a partitioning of unbound SFTI-1 across the BBB, K-p,K-uu,K-brain, of 13%, while only 0.5% of kalata B1 equilibrated across the BBB. By contrast, kalata B1, but not SFTI-1, readily entered neural cells. SFTI-1, but not kalata B1, could be a potential CNS delivery scaffold for drugs directed to extracellular targets. These findings indicate that differences between the BBB transport and cellular uptake abilities of CPPs are crucial in the development of peptide scaffolds.

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.

Solid lipid-based formulations (sLBFs) have the potential to increase the oral bioavailability of drugs with poor solubility in water, while counteracting some of the disadvantages of liquid LBFs. The most common experimental set-up to study the performance of LBFs in vitro is the lipolysis assay, during which the LBFs are digested by lipases in an environment mimicking the human small intestine. However, this assay has failed in many cases to correctly predict the performance of LBFs in vivo, highlighting the need for new and improved in vitro assays to evaluate LBFs at the preclinical stage. In this study, the suitability of three different in vitro digestion assays for the evaluation of sLBFs was assessed; the classic one-step intestinal digestion assay, a two-step gastrointestinal digestion assay and a bicompartmental assay permitting the simultaneous monitoring of digestion and permeation of the active pharmaceutical ingredient (API) across an artificial membrane (Lecithin in Dodecane - LiDo). Three sLBFs (M1-M3) with varied composition and ritonavir as model drug were prepared and examined. When comparing the ability of these formulations to keep the drug solubilized in the aqueous phase, all three assays show that M1 performs better, while M3 presents poor performance. However, the classic in vitro intestinal digestion assay fails to provide a clear ranking of the three formulations, something that is more evident when using the two modified and more physiologically relevant assays. Also, the two modified assays provide additional information about the performance of the formulations including the performance in the gastric environment and intestinal flux of the drug. These modified in vitro digestion assays are valuable tools for the development and evaluation of sLBFs to make better informed decisions of which formulations to pursue for in vivo studies.

Gliclazide was approved as a treatment for type 2 diabetes in an era before model-based drug development, and consequently, the recommended doses were not optimised with modern methods. To investigate various dosing regimens of gliclazide, we used publicly available data to characterise the dose-response relationship using pharmacometric models. A literature search identified 21 published gliclazide pharmacokinetic (PK) studies with full profiles. These were digitised, and a PK model was developed for immediate- (IR) and modified-release (MR) formulations. Data from a gliclazide dose-ranging study of postprandial glucose were used to characterise the concentration-response relationship using the integrated glucose-insulin model. Simulations from the full model showed that the maximum effect was 44% of the patients achieving HbA1c <7%, with 11% experiencing glucose <3 mmol/L and the most sensitive patients (i.e., 5% most extreme) experiencing 35 min of hypoglycaemia. Simulations revealed that the recommended IR dose (320 mg) was appropriate with no efficacy gain with increased dose. However, the recommended dose for the MR formulation may be increased to 270 mg, with more patients achieving HbA1c goals (i.e., HbA1c <7%) without a hypoglycaemic risk higher than the resulting risk from the recommended IR dose.

Background: Oxycodone active uptake across the blood-brain barrier (BBB) is associated with the putative proton-coupled organic cation (H+/OC) antiporter system. Yet, the activity of this system at the blood-cerebrospinal fluid barrier (BCSFB) is not fully understood. Additionally, sex differences in systemic pharmacokinetics and pharmacodynamics of oxycodone has been reported, but whether the previous observations involve sex differences in the function of the H+/OC antiporter system remain unknown. The objective of this study was, therefore, to investigate the extent of oxycodone transport across the BBB and the BCSFB in female and male Sprague-Dawley rats using microdialysis. Methods: Microdialysis probes were implanted in the blood and two of the following brain locations: striatum and lateral ventricle or cisterna magna. Oxycodone was administered as an intravenous infusion, and dialysate, blood and brain were collected. Unbound partition coefficients (K-p,K-uu) were calculated to understand the extent of oxycodone transport across the blood-brain barriers. Non-compartmental analysis was conducted using Phoenix 64 WinNonlin. GraphPad Prism version 9.0.0 was used to perform t-tests, one-way and two-way analysis of variance followed by Tukey's or Sidak's multiple comparison tests. Differences were considered significant at p < 0.05. Results: The extent of transport at the BBB measured in striatum was 4.44 +/- 1.02 (K-p,K-uu,K-STR), in the lateral ventricle 3.41 +/- 0.74 (K-p,K-uu,K-LV) and in cisterna magna 2.68 +/- 1.01 (K-p,K-uu,K-CM). These K-p,K-uu values indicate that the extent of oxycodone transport is significantly lower at the BCSFB compared with that at the BBB, but still confirm the presence of active uptake at both blood-brain interfaces. No significant sex differences were observed in neither the extent of oxycodone delivery to the brain, nor in the systemic pharmacokinetics of oxycodone. Conclusions: The findings clearly show that active uptake i

Model-based meta-analysis (MBMA) is an approach that integrates relevant summary level data from heterogeneously designed randomized controlled trials (RCTs). This study not only evaluated the predictability of a published MBMA for forced expiratory volume in one second (FEV1) and its link to annual exacerbation rate in patients with chronic obstructive pulmonary disease (COPD) but also included data from new RCTs. A comparative effectiveness analysis across all drugs was also performed. Aggregated level data were collected from RCTs published between July 2013 and November 2020 (n = 132 references comprising 156 studies) and combined with data used in the legacy MBMA (published RCTs up to July 2013 - n = 142). The augmented data (n = 298) were used to evaluate the predictive performance of the published MBMA using goodness-of-fit plots for assessment. Furthermore, the model was extended including drugs that were not available before July 2013, estimating a new set of parameters. The legacy MBMA model predicted the post-2013 FEV1 data well, and new estimated parameters were similar to those of drugs in the same class. However, the exacerbation model overpredicted the post-2013 mean annual exacerbation rate data. Inclusion of year when the study started on the pre-treatment placebo rate improved the model predictive performance perhaps explaining potential improvements in the disease management over time. The addition of new data to the legacy COPD MBMA enabled a more robust model with increased predictability performance for both endpoints FEV1 and mean annual exacerbation rate.

The interest for developing antibody-driven therapeutic interventions has exponentially grown over the last few decades. Even though there have been promising leaps in the development of efficacious antibody therapies, problems revolving around production and site-directed delivery of these large macromolecules persist. This is especially pertinent when it comes to designing and producing antibodies to penetrate the blood-brain barrier (BBB) to tackle neurodegenerative diseases. One of the most effective approaches to alleviating this problem is to employ a "Trojan Horse " approach, using receptor-mediated transcytosis, such as those governed by the transferrin receptor (TfR)-mediated pathways, to deliver large protein payloads into the brain. Even though this method is effective, ideal limiting factors, related to how the antibody binds to the TfR, need to be elucidated to improve BBB penetrance. With this said, we have designed and produced a single-chain Fc antibody, conjugated to an scFv8D3 TfR binding motif, creating a single-chain monovalent BBB transporter (scFc-scFv8D3). This recombinant protein is easy to produce and purify, demonstrates monovalent binding to the TfR and is structurally stable at physiologically relevant temperatures. Using an in vitro BBB model system, we show a positive correlation between the concentration of administered antibody and transcytosis efficacy, with scFc-scFv8D3 demonstrating significantly higher transcytosis levels compared with scFv8D3-conjugated bivalent antibodies at elevated administered concentrations. Furthermore, in vivo studies recapitulate the in vitro results, with the scFc-scFv8D3 demonstrating an elevated brain uptake at higher therapeutic doses in wild-type mice, comparable with that of the scFv8D3-conjugated bivalent antibody control. In addition, the half-life of the single-chain monovalent BBB transporter is comparable with that of standard IgG antibodies, indicating that the scFc format does not exacerbate, De två första författarna delar förstaförfattarskapet

Background Suboptimal exposure to antituberculosis (anti-TB) drugs has been associated with unfavourable treatment outcomes. We aimed to investigate estimates and determinants of first-line anti-TB drug pharmacokinetics in children and adolescents at a global level. Methods We systematically searched MEDLINE, Embase and Web of Science (1990–2021) for pharmacokinetic studies of first-line anti-TB drugs in children and adolescents. Individual patient data were obtained from authors of eligible studies. Summary estimates of total/extrapolated area under the plasma concentration–time curve from 0 to 24 h post-dose (AUC0–24) and peak plasma concentration (Cmax) were assessed with random-effects models, normalised with current World Health Organization-recommended paediatric doses. Determinants of AUC0–24 and Cmax were assessed with linear mixed-effects models. Results Of 55 eligible studies, individual patient data were available for 39 (71%), including 1628 participants from 12 countries. Geometric means of steady-state AUC0–24 were summarised for isoniazid (18.7 (95% CI 15.5–22.6) h·mg·L−1), rifampicin (34.4 (95% CI 29.4–40.3) h·mg·L−1), pyrazinamide (375.0 (95% CI 339.9–413.7) h·mg·L−1) and ethambutol (8.0 (95% CI 6.4–10.0) h·mg·L−1). Our multivariate models indicated that younger age (especially <2 years) and HIV-positive status were associated with lower AUC0–24 for all first-line anti-TB drugs, while severe malnutrition was associated with lower AUC0–24 for isoniazid and pyrazinamide. N-acetyltransferase 2 rapid acetylators had lower isoniazid AUC0–24 and slow acetylators had higher isoniazid AUC0–24 than intermediate acetylators. Determinants of Cmax were generally similar to those for AUC0–24. Conclusions This study provides the most comprehensive estimates of plasma exposures to first-line anti-TB drugs in children and adolescents. Key determinants of drug exposures were identified. These may be relevant for population-specific dose adjustment or individualis

Pharmaceutical formulation of oral dosage forms is continuously challenged by the low solubility of new drug candidates. Pickering emulsions, emulsions stabilized with solid particles, are a promising alternative to surfactants for developing long-term stable emulsions that can be tailored for controlled release of lipophilic drugs. In this work, a non-emulsifying lipid-based formulation (LBF) loaded with fenofibrate was formulated into an oilin-water (O/W) emulsion synergistically stabilized by stearic acid and silica (SiO2) nanoparticles. The emulsion had a droplet size of 341 nm with SiO2 particles partially covering the oil-water interface. In vitro lipid digestion was faster for the emulsion compared to the corresponding LBF due to the larger total surface area available for digestion. Cellulose biopolymers were added to the emulsion to produce a gel for semi-solid extrusion (SSE) 3D printing into tablets. The emulsion gel showed suitable rheological attributes for SSE, with a trend of higher viscosity, yield stress, and storage modulus (G & PRIME;), compared to a conventional self-emulsifying lipid-based emulsion gel. The developed emulsion gel allows for a non-emulsifying LBF to be transformed into solid dosage forms for rapid lipid digestion and drug release of a poorly water-soluble drug in the small intestine.

This study aimed to evaluate a material sparing method to predict tabletability and compactibility relationships. Seven a-lactose monohydrate powders with varying particle size were used as test materials. The compressibility of the powders was determined experimentally, whereas tabletability and compactibility profiles were derived both experimentally and predicted. In the prediction method, two experimental compression parameters (Kawakita b-1 and Heckel plastic stiffness) and a single tensile strength reference value were used, all necessary data obtained from a single compression experiment. For both predicted and experimental relationships, compaction and tableting parameters (performance indicators) were calculated. The correction for viscoelastic recovery was successful in generating compressibility profiles that corresponded to the series of experimental out-of-die tablet porosities. For both the tabletability and compactibility, the experimental and predicted profiles showed a high degree of similarity. Good correlations were obtained between the predicted and experimental compaction and tableting parameters. It is concluded that the hybrid prediction method is a material sparing method, which can give good approximations of tabletability and compactibility relationships. The prediction method has the potential to be included as a part of a protocol for the characterisation of the tableting performance of particulate solids.

The blood-brain barrier (BBB) poses major challenges to drug delivery to the CNS. SFTI-1 and kalata B1 are cyclic cell-penetrating peptides (cCPPs) with high potential to be used as scaffolds for drug delivery. We here studied their transport across the BBB and distribution within the brain to gauge the potential of these two cCPPs as scaffolds for CNS drugs. In a rat model, SFTI-1 exhibited, for a peptide, high extent of BBB transport with a partitioning of unbound SFTI-1 across the BBB, K-p,K-uu,K-brain, of 13%, while only 0.5% of kalata B1 equilibrated across the BBB. By contrast, kalata B1, but not SFTI-1, readily entered neural cells. SFTI-1, but not kalata B1, could be a potential CNS delivery scaffold for drugs directed to extracellular targets. These findings indicate that differences between the BBB transport and cellular uptake abilities of CPPs are crucial in the development of peptide scaffolds.

In this work, simulations of intestinal peristalsis are performed to investigate the intraluminal transport of macromolecules (MMs) and permeation enhancers (PEs). Properties of insulin and sodium caprate (C10) are used to represent the general class of MM and PE molecules. Nuclear magnetic resonance spectroscopy was used to obtain the diffusivity of C10, and coarse-grain molecular dynamics simulations were carried out to estimate the concentration-dependent diffusivity of C10. A segment of the small intestine with the length of 29.75 cm was modeled. Peristaltic speed, pocket size, release location, and occlusion ratio of the peristaltic wave were varied to study the effect on drug transport. It was observed that the maximum concentration at the epithelial surface for the PE and the MM increased by 397 % and 380 %, respectively, when the peristaltic wave speed was decreased from 1.5 to 0.5 cm s−1. At this wave speed, physiologically relevant concentrations of PE were found at the epithelial surface. However, when the occlusion ratio is increased from 0.3 to 0.7, the concentration approaches zero. These results suggest that a slower-moving and more contracted peristaltic wave leads to higher efficiency in transporting mass to the epithelial wall during the peristalsis phases of the migrating motor complex.

A milestone in the development of novel antituberculosis drugs is the demonstration of early bactericidal activity (EBA) in a phase IIa clinical trial. The significant variability in measurements of bacterial load complicates data analysis in these trials. A systematic review and evaluation of methods for determination of EBA in pulmonary tuberculosis studies was undertaken. Bacterial load quantification biomarkers, reporting intervals, calculation methods, statistical testing, and handling of negative culture results were extracted. In total, 79 studies were identi-fied in which EBA was determined. Colony-forming units on solid culture media and/or time-to-positivity in liquid media were the biomarkers used most often, reported in 72 (91%) and 34 (43%) studies, respec-tively. Twenty-two different reporting intervals were presented, and 12 different calculation methods for EBA were identified. Statistical testing for a significant EBA compared with no change was performed in 54 (68%) studies, and between-group testing was performed in 32 (41%) studies. Negative culture result handling was discussed in 34 (43%) studies. Notable variation was found in the analysis methods and reporting of EBA studies. A standardized and clearly reported analysis method, accounting for different levels of variability in the data, could aid the generalization of study results and facilitate comparison between drugs/regimens.

Background Higher doses of rifampicin for tuberculosis have been shown to improve early bactericidal activity (EBA) and at the same time increase the intolerability due to high exposure at the beginning of treatment. To support dose optimisation of rifampicin, this study investigated new and innovative staggered dosing of rifampicin using clinical trial simulations to minimise tolerability problems and still achieve good efficacy. Methods Rifampicin population pharmacokinetics and time-to-positivity models were applied to data from patients receiving 14 days of daily 10–50 mg/kg rifampicin to characterise the exposure-response relationship. Furthermore, clinical trial simulations of rifampicin exposure were performed following four different staggered dosing scenarios. The simulated exposure after 35 mg/kg was used as a relative comparison for efficacy. Tolerability was derived from a previous model-based analysis relating exposure at day 7 and the probability of having adverse events. Results The linear relationship between rifampicin exposure and bacterial killing rate in sputum indicated that the maximum rifampicin EBA was not reached at doses up to 50 mg/kg. Clinical trial simulations of a staggered dosing strategy starting the treatment at a lower dose (20 mg/kg) for 7 days followed by a higher dose (40 mg/kg) predicted a lower initial exposure with lower probability of tolerability problems and better EBA compared with a regimen of 35 mg/kg daily. Conclusions Staggered dosing of 20 mg/kg for 7 days followed by 40 mg/kg is predicted to reduce tolerability while maintaining exposure levels associated with better efficacy.

Background: Linezolid is an effective, but toxic anti-tuberculosis drug that is currently recommended for the treatment of drug-resistant tuberculosis. Improved oxazolidinones should have a better safety profile, while preserving efficacy. Delpazolid is a novel oxazolidinone developed by LegoChem Biosciences Inc. that has been evaluated up to phase 2a clinical trials. Since oxazolidinone toxicity can occur late in treatment, LegoChem Biosciences Inc. and the PanACEA Consortium designed DECODE to be an innovative dose-ranging study with long-term follow-up for determining the exposure-response and exposure-toxicity relationship of delpazolid to support dose selection for later studies. Delpazolid is administered in combination with bedaquiline, delamanid and moxifloxacin. Methods: Seventy-five participants with drug-sensitive, pulmonary tuberculosis will receive bedaquiline, delamanid and moxifloxacin, and will be randomized to delpazolid dosages of 0 mg, 400 mg, 800 mg, 1200 mg once daily, or 800 mg twice daily, for 16 weeks. The primary efficacy endpoint will be the rate of decline of bacterial load on treatment, measured by MGIT liquid culture time to detection from weekly sputum cultures. The primary safety endpoint will be the proportion of oxazolidinone class toxicities; neuropathy, myelosuppression, or tyramine pressor response. Participants who convert to negative liquid media culture by week 8 will stop treatment after the end of their 16-week course and will be observed for relapse until week 52. Participants who do not convert to negative culture will receive continuation phase treatment with rifampicin and isoniazid to complete a six-month treatment course. Discussion: DECODE is an innovative dose-finding trial, designed to support exposure-response modelling for safe and effective dose selection. The trial design allows assessment of occurrence of late toxicities as observed with linezolid, which is necessary in clinical evaluation of novel oxazolidinone

Although tuberculosis (TB) is preventable and curable, the lengthy treatment (generally 6 months), poor patient adherence, high inter-individual variability in pharmacokinetics (PK), emergence of drug resistance, presence of comorbidities, and adverse drug reactions complicate TB therapy and drive the need for new drugs and/or regimens. Hence, new compounds are being developed, available drugs are repurposed, and the dosing of existing drugs is optimized, resulting in the largest drug development portfolio in TB history. This review highlights a selection of clinically available drug candidates that could be part of future TB regimens, including bedaquiline, delamanid, pretomanid, linezolid, clofazimine, optimized (high dose) rifampicin, rifapentine, and para-aminosalicylic acid. The review covers drug development history, preclinical data, PK, and current clinical development.

Blood pressure measurements form a critical component of adverse event monitoring for tyrosine kinase inhibitors, but might also serve as a biomarker for dose titrations. This study explored the impact of various sources of within-individual variation on blood pressure readings to improve measurement practices and evaluated the utility for individual- and population-level dose selection. A pharmacokinetic-pharmacodynamic modeling framework was created to describe circadian blood pressure changes, inter- and intra-day variability, changes from dipper to non-dipper profiles, and the relationship between drug exposure and blood pressure changes over time. The framework was used to quantitatively evaluate the influence of physiological and pharmacological aspects on blood pressure measurements, as well as to compare measurement techniques, including office-based, home-based, and ambulatory 24-h blood pressure readings. Circadian changes, as well as random intra-day and inter-day variability, were found to be the largest sources of within-individual variation in blood pressure. Office-based and ambulatory 24-h measurements gave rise to potential bias (>5 mmHg), which was mitigated by model-based estimations. Our findings suggest that 5-8 consecutive, home-based, measurements taken at a consistent time around noon, or alternatively within a limited time frame (e.g., 8.00 a.m. to 12.00 p.m. or 12.00 p.m. to 5.00 p.m.), will give rise to the most consistent blood pressure estimates. Blood pressure measurements likely do not represent a sufficiently accurate method for individual-level dose selection, but may be valuable for population-level dose identification. A user-friendly tool has been made available to allow for interactive blood pressure simulations and estimations for the investigated scenarios.

Title in the list of papers of Estelle Chasseloup thesis: Comparison of seven non-linear mixed effect model-based approaches to test for drug effect

The clinical support system Electronic Expert Support (EES) is available at all pharmacies in Sweden to examine electronic prescriptions when dispensing to prevent drug-related problems (DRPs). DRPs are common, and result in patient suffering and substantial costs for society. The aim of this research was to study the use of EES for the pediatric population (ages 0-12 years), by describing what types of alerts are generated for potential DRPs, how they are handled, and how the use of EES has changed over time. Data on the number and categories of EES analyses, alerts, and resolved alerts were provided by the Swedish eHealth Agency. The study shows that the use of EES has increased. The most common type of alert for a potential DRP among pediatric patients was regarding high doses in children (30.3% of all alerts generated). The most common type of alert for a potential DRP that was resolved among pediatrics was therapy duplication (4.6% of the alerts were resolved). The most common reason for closing an alert was dialogue with patient for verification of the treatment (66.3% of all closed alerts). Knowledge of which type of alerts are the most common may contribute to increased prescriber awareness of important potential DRPs.

Medication adherence is a key precondition of the effectiveness of evidence-based therapies. However, in real-life settings, non-adherence to medication is still very common. This leads to profound health and economic consequences at both individual and public health levels. The problem of non-adherence has been extensively studied in the last 50 years. Unfortunately, with more than 130,000 scientific papers published on that subject so far, we are still far from finding an ultimate solution. This is, at least partly, due to fragmented and poor-quality research that has been conducted in this field sometimes. To overcome this deadlock, there is a need to stimulate the adoption of best practices in medication adherence-related research in a systematic way. Therefore, herein we propose the establishment of dedicated medication adherence research Centres of Excellence (CoEs). These Centres could not only conduct research but could also create a profound societal impact, directly serving the needs of patients, healthcare providers, systems and economies. Additionally, they could play a role as local advocates for good practices and education. In this paper, we propose some practical steps that might be taken in order to establish such CoEs. We describe two success stories, i.e., Dutch and Polish Medication Adherence Research CoEs. The COST Action "European Network to Advance Best practices & technoLogy on medication adherencE" (ENABLE) aims to develop a detailed definition of the Medication Adherence Research CoE in the form of a list of minimal requirements regarding their objectives, structure and activities. We hope that it will help to create a critical mass and catalyse the setup of regional and national Medication Adherence Research CoEs in the near future. This, in turn, may not only increase the quality of the research but also raise the awareness of non-adherence and promote the adoption of the best medication adherence-enhancing interventions.

Biomarkers are quantifiable characteristics of biological processes. In Mycobacterium tuberculosis, common biomarkers used in clinical drug development are colony forming unit (CFU) and time-to-positivity (TTP) from sputum samples. This analysis aimed to develop a combined quantitative tuberculosis biomarker model for CFU and TTP biomarkers for assessing drug efficacy in early bactericidal activity studies. Daily CFU and TTP observations in 83 previously patients with uncomplicated pulmonary tuberculosis after 7 days of different rifampicin monotherapy treatments (10-40 mg/kg) from the HIGHRIF1 study were included in this analysis. The combined quantitative tuberculosis biomarker model employed the Multistate Tuberculosis Pharmacometric model linked to a rifampicin pharmacokinetic model in order to determine drug exposure-response relationships on three bacterial sub-states using both the CFU and TTP data simultaneously. CFU was predicted from the MTP model and TTP was predicted through a time-to-event approach from the TTP model, which was linked to the MTP model through the transfer of all bacterial sub-states in the MTP model to a one bacterial TTP model. The non-linear CFU-TTP relationship over time was well predicted by the final model. The combined quantitative tuberculosis biomarker model provides an efficient approach for assessing drug efficacy informed by both CFU and TTP data in early bactericidal activity studies and to describe the relationship between CFU and TTP over time.

A dynamic epithelium and a rich microbiota, separated by multi-layered mucus, make up the complex colonic cellular environment. Both cellular systems are characterized by high inter-and intraindividual differences, but their impact on drug distribution and efficacy remains incompletely understood. This research gap is pressing, as, e.g., inflammatory disorders of the colon are on the rise globally. In an effort to help close this gap, we provide considerations on determining colonic epithelial and microbial cellular parameters, and their impact on drug bioavailability. First, we cover the major cell types found in vivo within the epithelium and microbiota, and discuss how they can be modeled in vitro. We then draw attention to their structural similarities and differences with regard to determinants of drug distribution. Once a drug is solubilized in the luminal fluids, there are two main classes of such determinants: 1) binding processes, and 2) transporters and drug-metabolizing enzymes. Binding lowers the unbound intracellular fraction (fu,cell), which will, in turn, limit the amount of drug available for transport to desired sites. Transporters and drug metabolizing enzymes are ADME proteins impacting intracellular accumulation (Kp). Across cell types, we point out which processes are likely particularly impactful. Together, fu,cell and Kp can be used to describe intracellular bioavailability (Fic), which is a measure of local drug distribution, with consequences for efficacy. Determining these cellular parameters will be beneficial in un-derstanding colonic drug distribution and will advance the field of drug delivery.

Background: Multi-dose drug dispensing (MDD) is an alternative to assist patients with difficulties in administering and handling their medications. In 2019 the primary care units at Region Gotland reported a high number of discrepancies, regarding patients with MDD discharged from the Hospital of Visby. Due to the high proportion of discrepancies between the patient's medication list in TakeCare and the prescription list of MDD the clinical pharmacists at the hospital began to check all patients marked for discharge in order to discover and prevent eventual discrepancies. Aim: To identify discrepancies between the patient’s medication list and the prescription for MDD when discharged from the Hospital of Visby and investigate whether there has been a decrease since 2019. Method: A quantitative cross-sectional study was performed. Medication lists from each patient’s journal and their prescribing record from the prescription system for MDD were reviewed to identify discrepancies, which then were noted and discussed with the physician in charge. Identified discrepancies were rated into pre-defined categories and compared with data from previous years. A chi-square test was performed to investigate the significant difference of discrepancies between the units. The results from the intervention were summarized into figures and tables. Results: Medication lists from 144 patients with a mean age of 83 years were included. Discrepancies were identified in 34% of the patients and a total of 98 discrepancies were noted, which proves a significant decrease since 2019 when the number of discrepancies was 70%. Totally 79% of the discrepancies were estimated to have occurred during the patient's hospital stay. The highest number of discrepancies were found in the medical department and the difference between all inpatient care departments was significant (p <0.0001). Conclusion: The results indicate a decrease in discrepancies since 2019. Further research is necessary to cla

Background: There are some health care centers in Region Västmanland that always turn a profit, and other health care centers which always go at a loss. Aim: The aim of this study was to analyze the potential reasons as to why the same health care centers always profit, and the other health care centers always go at a loss under year 2022. Methods: A cross-sectional study with register data for the year 2022 was used as a method. The analysis was performed by aggregating different indicators and measures. For example, the age and sex of the patients, socioeconomic status, morbidity, and number of listed patients that the health centers have. Two different therapy areas were chosen for deeper analysis - diabetes and atrial fibrillation. Medicines for these diagnoses have the highest cost for the health centers. In addition to this, differences between the health care centers in prescribing of over-the-counter medicines and in selection of generic drugs were also considered. Results: The results show that the three health centers that lost the most had 29 percent higher costs per diabetes patient of the more expensive diabetes drugs compared to other three health centers that always make a profit. The health centers that had the largest loss had higher costs of Lyrica, a more expensive drug compared to other bioequivalent drugs, than the other health centers. The health centers that lost the most had older patients, and more patients who were socioeconomically disadvantaged. Two of the health centers that lost the most had higher CNI scores, which means that the patients at these health centers had a lower socio-economic status. In conclusion, the health care centers that lost the most had the highest diabetes costs, especially from the more expensive drugs and they had also more older patients.

Background: A negative outcome of consuming and manufacturing pharmaceuticals is that these substances reach the environment through different pathways. This can lead to severe consequences such as antibiotic resistance, but also disturbances in entire ecosystems. Educating health care professionals such as doctors and pharmacists about the subject could contribute to more environmentally friendly, pharmaceutical-related decisionsto be taken in the future, resulting in less negative environmental impacts. Aim: To describe Swedish universities' educations of medical and pharmacy programs regarding the impact of pharmaceuticals on the environment, as well as how the students in their future professional role can contribute to reduce this impact. Method: A survey study consisting of two parts. One questionnaire-based part, and one part reviewing educational plans for pharmacy and medical programs. One questionnaire was sent to course managers for courses within pharmacy programs, and one to program managers for the medical programs. Results: In total, 47 out of 100 course managers and 6 out of 7 program managers participated in the questionnaires. 19 course managers indicated that their courses within pharmacy programs contain education about the environmental impacts of pharmaceuticals. All 6 program managers indicated that their program contains education about this. The most common areas educated about were sustainable drug use, sustainable drug manufacturing and development. All education plans contained learning objectives related to the environmental impacts of pharmaceuticals. Conclusion: Sweden’s pharmacy and medical programs do contain some education about the environmental impacts of pharmaceuticals. However, more education is needed, especially regarding knowledge databases.

The gold standard for pharmacometrics modeling, along with its modeling format, is currently NONMEM. In order to use other software, there is often a manual step of converting a model from one format to another, which is both time-consuming and causes manual errors. This project aimed to solve this problem by creating a conversion- and validation tool from NONMEM to two formats: nlmixr2 and RxODE2. These are both, unlike NONMEM, freely available and integrated into R. This was done by integrating the two tools (conversion and validation) into the program Pharmpy, which can extract model information from NONMEM's model format. For conversion, the model was read into Pharmpy and then, part by part, converted to the respective model format. The associated validation compared the predictions of the respective programs to see if they differed significantly from each other. The project showed that this type of conversion is possible, but the programs showed a greater difference than expected. Part of this can be explained by a rounding of parameter values in Pharmpy, but further analysis also indicated fundamental differences in the determination of the predictions between the programs. Larger differences in predictions are for instance oftentimes equidistant from the actual observation, meaning the predictions are presumably calculated differently. While not disproving the converted model, smaller discrepancies between the programs would indicate a more confident validation. In summary, the developed tools are considered useful for models where Pharmpy supports parsing of the model. If not for complete conversion, then at least for partial conversion with manual correction, which is also an improvement over an entirely manual workflow.

Gender differences in drug use in Sweden – a systematic analysis of changes taking place during a decade Background: From a global perspective, Sweden upholds a high-quality healthcare provided on equal terms. Despite this, there are differences in health and living conditions as well as drug utilization between men and women. Aim: The aim of this study was to analyze how gender differences in drug use in Sweden have changed over the past 10 years. Specifically, to observe within which pharmacological groups large increases and decreases have taken place between 2011 and 2021. Method: A quantitative cross-sectional study was conducted to compare the prevalence of drug use between women and men in 2011 and 2021. The study used data from the Swedish Prescribed drug register containing information about all dispensed prescription drugs. Results: The gender differences differ depending on whether the differences are analyzed in relative or absolute terms. The largest sex difference in absolute numbers was antibiotics with a greater use were in women, while lipid lowering agents were showed the highest use among males. The largest increase in relative gender difference from 2011 to 2021 was for antimigrane preparations while the relative gender differences has decreased most substantially for mineral substances. Conclusion: The study shows that gender differences still exist and have not decreased in the last 10 years. Some findings on gender differences could be explained using research on prevalence of diseases between the sexes, while some differences could not be justified using medical explanations. Unexplainable gender differences can be explained by normative structures that exist and affect healthcare in Swedish society.

Background: Pharmaceuticals are a big part of healthcare expenditures. Advancements such as the sequencing of the human genome and technical methods has led to the development of more advanced drugs. Aim: The aim of this study was to explore the trends in new drug development areas and the costs of drugs in different therapy areas. Method: To do this, data from the Swedish Medical Products Agency, e-Hälsomyndigheten and the 20 biggest pharmaceutical companies regarding new approved drugs during a 20-year period, costs and new chemical entities in late development stage has been analyzed. Results: Each year, between 20 and 60 new drugs are approved. New chemical entities are most prevalent in the ATC-group L, which includes oncology and immunomodulating drugs. Innovation is also seen in group J, antiinfectives. The cost of drugs has increased during the study period, especially for oncology drugs. In the coming years, we can expect the introduction of even more oncology drugs, as mostdrugs in later development phases are expected to be classified in this group. Conclusion: Over the studied period, the most innovative therapeutic group has not changed but rather grown. The cost of drugs has also grown, possibly because of more new drugs introduced but also due to expanded indications and larger patient groups. It is hard to see a future where oncology drugs are not the biggest cost since in the coming years, many of the expected new drugs will fall into the same category.

Background Suboptimal exposure to antituberculosis (anti-TB) drugs has been associated with unfavourable treatment outcomes. We aimed to investigate estimates and determinants of first-line anti-TB drug pharmacokinetics in children and adolescents at a global level. Methods We systematically searched MEDLINE, Embase and Web of Science (1990–2021) for pharmacokinetic studies of first-line anti-TB drugs in children and adolescents. Individual patient data were obtained from authors of eligible studies. Summary estimates of total/extrapolated area under the plasma concentration–time curve from 0 to 24 h post-dose (AUC0–24) and peak plasma concentration (Cmax) were assessed with random-effects models, normalised with current World Health Organization-recommended paediatric doses. Determinants of AUC0–24 and Cmax were assessed with linear mixed-effects models. Results Of 55 eligible studies, individual patient data were available for 39 (71%), including 1628 participants from 12 countries. Geometric means of steady-state AUC0–24 were summarised for isoniazid (18.7 (95% CI 15.5–22.6) h·mg·L−1), rifampicin (34.4 (95% CI 29.4–40.3) h·mg·L−1), pyrazinamide (375.0 (95% CI 339.9–413.7) h·mg·L−1) and ethambutol (8.0 (95% CI 6.4–10.0) h·mg·L−1). Our multivariate models indicated that younger age (especially <2 years) and HIV-positive status were associated with lower AUC0–24 for all first-line anti-TB drugs, while severe malnutrition was associated with lower AUC0–24 for isoniazid and pyrazinamide. N-acetyltransferase 2 rapid acetylators had lower isoniazid AUC0–24 and slow acetylators had higher isoniazid AUC0–24 than intermediate acetylators. Determinants of Cmax were generally similar to those for AUC0–24. Conclusions This study provides the most comprehensive estimates of plasma exposures to first-line anti-TB drugs in children and adolescents. Key determinants of drug exposures were identified. These may be relevant for population-specific dose adjustment or individualis