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The selection of appropriate starting dose and suitable method to predict an efficacious dose for novel oncology drug in the early clinical development stage poses significant challenges. The traditional methods of using body surface area transformation from toxicology studies to predict the first-in human (FIH) starting dose, or simply selecting the maximum tolerated dose (MTD) or maximum administered dose (MAD) as efficacious dose or recommended phase 2 dose (RP2D), are usually inadequate and risky for novel oncology drugs. Due to the regulatory efforts aimed at improving dose optimisation in oncology drug development, clinical dose selection is now shifting away from these traditional methods towards a comprehensive benefit/risk assessment-based approach. Quantitative pharmacology analysis (QPA) plays a crucial role in this new paradigm. This mini-review summarises the use of QPA in selecting the starting dose for oncology FIH studies and potential efficacious doses for expansion or phase 2 trials. QPA allows for a more rational and scientifically based approach to dose selection by integrating information across studies and development phases. In conclusion, the application of QPA in oncology drug development has the potential to significantly enhance the success rates of clinical trials and ultimately support clinical decision-making, particularly in dose selection. [ABSTRACT FROM AUTHOR]

Immuno-oncology (IO) therapies have changed the cancer treatment landscape. Immune checkpoint inhibitors (ICIs) have improved overall survival in 20–40% of patients with malignancies that were previously refractory. Due to the uniqueness in biology, modalities and patient responses, drug development strategies for IO differed from that traditionally used for cytotoxic and target therapies in oncology, and quantitative pharmacology utilizing modeling approach can be applied in all phases of the development process. In this review, we used case studies to showcase how various modeling methodologies were applied from translational science and dose selection through to label change, using examples that included anti-programmed-death-1 (anti-PD-1), anti-programmed-death ligand-1 (anti-PD-L1), anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA-4), and anti-glucocorticoid-induced tumor necrosis factor receptor-related protein (anti-GITR) antibodies. How these approaches were utilized to support phase I–III dose selection, the design of phase III trials, and regulatory decisions on label change are discussed to illustrate development strategies. Model-based quantitative approaches have positively impacted IO drug development, and a better understanding of the biology and exposure–response relationship may benefit the development and optimization of new IO therapies. [ABSTRACT FROM AUTHOR]

The interpretation of immunogenicity results for a mAb product and prediction of its clinical consequences remain difficult, despite enormous advances in methodologies and efforts toward the best practice for consistent data generation and reporting. To this end, the contribution from the clinical pharmacology discipline has been largely limited to comparing descriptively the pharmacokinetic (PK) profiles by antidrug antibodies (ADA) status or testing the significance of ADA as a covariate in a population PK setting, similar to the practice for small-molecule drugs in investigating the effect of an intrinsic/extrinsic factor on the drug disposition. There is a need for a mAb disposition framework that captures the dynamics of ADA formation and drug's interactions with the ADA and target as parts of the drug distribution and elimination. Here we describe such a framework and examine it against the PK, ADA, and clinical response data from a phase 3 trial in patients treated with adalimumab. The proposed framework offered a generalized understanding of how the dose, target affinity, and drug/ADA analyte forms affects the manifestation of ADA response with regard to its detections and alterations of drug disposition and effectiveness. Furthermore, as an example, its utility for dose considerations was demonstrated through predicting for late-stage trials of a PCSK9 inhibitor in terms of development in ADA incidence and titers, and consequences on the drug disposition, interaction with target, and downstream lowering effect on LDL-C. [ABSTRACT FROM AUTHOR]

Objectives: To investigate whether exposure history to two common loop diuretics, bumetanide and furosemide, affects the risk of developing Alzheimer’s disease (AD) after accounting for socioeconomic status and congestive heart failure.Methods: Individuals exposed to bumetanide or furosemide were identified in the Stanford University electronic health record using the de-identified Observational Medical Outcomes Partnership platform. We matched the AD case cohort to a control cohort (1:20 case:control) on gender, race, ethnicity, and hypertension, and controlled for variables that could potentially be collinear with bumetanide exposure and/or AD diagnosis. Among individuals older than 65 years, 5,839 AD cases and 116,103 matched controls were included. A total of 1,759 patients (54 cases and 1,705 controls) were exposed to bumetanide.Results: After adjusting for socioeconomic status and other confounders, the exposure of bumetanide and furosemide was significantly associated with reduced AD risk (respectively, bumetanide odds ratio [OR] = 0.23; 95% confidence interval [CI], 0.15–0.36; p = 4.0 × 10−11; furosemide OR = 0.42; 95% CI, 0.38–0.47; p < 2.0 × 10−16).Discussion: Our study replicates in an independent sample that a history of bumetanide exposure is associated with reduced AD risk while also highlighting an association of the most common loop diuretic (furosemide) with reduced AD risk. These associations need to be additionally replicated, and the mechanism of action remains to be investigated.

Model-Informed Drug Discovery and Development (MID3) represents a transformative approach in pharmaceutical research, integrating quantitative models to inform and optimize decision-making throughout the drug development process. This review explores the current applications, challenges, and future prospects of MID3 within the Middle East and North Africa (MENA) region. By leveraging local data and advanced computational techniques, MID3 has the potential to significantly enhance the efficiency and success rates of drug development tailored to regional health priorities. We discussed successful case studies of applying MID3 at different phases of drug development and clinical trials. Furthermore, we emphasized the critical need for MENA countries to embrace MID3 by investing in workforce training, aligning regulatory frameworks, and fostering collaborative research initiatives. This call to action underscores the importance of a robust MID3 ecosystem, urging policymakers, academic institutions, and industry stakeholders to prioritize and support its integration into the MENA region's healthcare. [ABSTRACT FROM AUTHOR]

Background: Atezolizumab has been studied in multiple indications for both pediatric and adult patient populations. Generally, clinical studies enrolling pediatric patients may not collect sufficient pharmacokinetic data to characterize the drug exposure and disposition because of operational, ethical, and logistical challenges including burden to children and blood sample volume limitations. Therefore, mechanistic modeling and simulation may serve as a tool to predict and understand the drug exposure in pediatric patients. Objective: To use mechanistic physiologically-based pharmacokinetic (PBPK) modeling to predict atezolizumab exposure at a dose of 15 mg/kg (max 1,200 mg) in pediatric patients to support dose rationalization and label recommendations. Methods: A minimal mechanistic PBPK model was used which incorporated age-dependent changes in physiology and biochemistry that are related to atezolizumab disposition such as endogenous IgG concentration and lymph flow. The PBPK model was developed using both in vitro data and clinically observed data in adults and was verified across dose levels obtained from a phase I and multiple phase III studies in both pediatric patients and adults. The verified model was then used to generate PK predictions for pediatric and adult subjects ranging from 2- to 29-year-old. Results: Individualized verification in children and in adults showed that the simulated concentrations of atezolizumab were comparable (76% within twofold and 90% within three-fold, respectively) to the observed data with no bias for either over- or under-prediction. Applying the verified model, the predicted exposure metrics including Cmin, Cmax, and AUCtau were consistent between pediatric and adult patients with a geometric mean of pediatric exposure metrics between 0.8- to 1.25-fold of the values in adults. Conclusion: The results show that a 15 mg/kg (max 1,200 mg) atezolizumab dose administered intravenously in pediatric patients provides comparable atezolizumab exposure to a dose of 1,200 mg in adults. This suggests that a dose of 15 mg/kg will provide adequate and effective atezolizumab exposure in pediatric patients from 2- to 18-year-old. [ABSTRACT FROM AUTHOR]

CD3-bispecific antibodies are a new class of immunotherapeutic drugs against cancer. The pharmacological activity of CD3-bispecifics is typically assessed through in vitro assays of cancer cell lines co-cultured with human peripheral blood mononuclear cells (PBMCs). Assay results depend on experimental conditions such as incubation time and the effector-to-target cell ratio, which can hinder robust quantification of pharmacological activity. In order to overcome these limitations, we developed a new, holistic approach for quantification of the in vitro dose–response relationship. Our experimental design integrates a time-independent analysis of the dose–response across different time points as an alternative to the static, "snap-shot" analysis based on a single time point commonly used in dose–response assays. We show that the potency values derived from static in vitro experiments depend on the incubation time, which leads to inconsistent results across multiple assays and compounds. We compared the potency values from the time-independent analysis with a model-based approach. We find comparably accurate potency estimates from the model-based and time-independent analyses and that the time-independent analysis provides a robust quantification of pharmacological activity. This approach may allow for an improved head-to-head comparison of different compounds and test systems and may prove useful for supporting first-in-human dose selection. [ABSTRACT FROM AUTHOR]

Mathematical modeling has been an important tool in pharmaceutical research for 50 + years and there is increased emphasis over the last decade on using modeling to improve the efficiency and effectiveness of drug development. In an earlier commentary, we applied a multiscale model linking 6 scales (whole body, tumor, vasculature, cell, spatial location, time), together with literature data on nanoparticle and tumor properties, to demonstrate the effects of nanoparticle particles on systemic disposition. The current commentary used a 4-scale model (cell membrane, intracellular organelles, spatial location, time) together with literature data on the intracellular processing of membrane receptors and transporters to demonstrate disruption of transporter homeostasis can lead to drug-drug interaction (DDI) between victim drug (VD) and perpetrator drug (PD), including changes in the area-under-concentration–time-curve of VD in cells that are considered significant by the US Food and Drug Administration (FDA). The model comprised 3 computational components: (a) intracellular transporter homeostasis, (b) pharmacokinetics of extracellular and intracellular VD/PD concentrations, and (c) pharmacodynamics of PD-induced stimulation or inhibition of an intracellular kinetic process. Model-based simulations showed that (a) among the five major endocytic processes, perturbation of transporter internalization or recycling led to the highest incidence and most extensive DDI, with minor DDI for perturbing transporter synthesis and early-to-late endosome and no DDI for perturbing transporter degradation and (b) three experimental conditions (spatial transporter distribution in cells, VD/PD co-incubation time, extracellular PD concentrations) were determinants of DDI detection. We propose modeling is a useful tool for hypothesis generation and for designing experiments to identify potential DDI; its application further aligns with the model-informed drug development paradigm advocated by FDA. [ABSTRACT FROM AUTHOR]

Abstract Background Dexamethasone is used for the intra-articular route of administration in management of aseptic arthritis in horses. Despite its widespread use there is very little quantitative data of the disposition and response to dexamethasone. The aim of this study was to investigate and describe the synovial fluid and plasma dexamethasone concentration over time and to explore the relation between synovial fluid concentration and response using clinical endpoints as response biomarkers after IA injection of dexamethasone disodium salt solution in an equine model of synovitis. Results Inflammation was induced in the radiocarpal joint of six horses by injection of 2 ng lipopolysaccharide (LPS). Two hours later either saline or dexamethasone was injected in the same joint in a two treatment cross over design. Each horse was treated once with one of the six doses dexamethasone used (0.01, 0.03, 0.1, 0.3, 1 or 3 mg) and once with saline. Dexamethasone was quantified by means of UHPLC–MS/MS. Dexamethasone disposition was characterised by means of a non-linear mixed effects model. Lameness was evaluated both objectively with an inertial sensor based system and subjectively scored using a numerical scale (0–5). Joint circumference, skin temperature over the joint and rectal temperature were also recorded. The LPS-challenge induced lameness in all horses with high inter-individual variability. Dexamethasone significantly decreased lameness compared with saline. Other variables were not statistically significant different between treatments. Objective lameness scoring was the most sensitive method used in this study to evaluate the lameness response. A pharmacokinetic/pharmacodynamic model was successfully fitted to experimental dexamethasone and lameness data. The model allowed characterization of the dexamethasone synovial fluid concentration–time course, the systemic exposure to dexamethasone after intra-articular administration and the concentration–response relation in an experimental model of synovitis. Conclusions The quantitative data improve the understanding of the pharmacology of dexamethasone and might serve as input for future experiments and possibly contribute to maintain integrity of equine sports.

Background/Aims: Traditional Chinese medicine (TCM) has been used in clinical practice for thousands of years and has accumulated considerable knowledge concerning the in vivo efficacy of targeting complicated diseases. TCM formulae are a mixture of hundreds of chemical components with multiple potential targets, essentially acting as a combination therapy of multi-component drugs. However, the obscure substances and the unclear molecular mechanisms are obstacles to their further development and internationalization. Therefore, it is necessary to develop new modern drugs based on the combination of effective components in TCM with exact clinical efficacy. In present study, we aimed to detect optimal ratio of the combination of effective components based on Sheng-Mai-San for myocardial ischemia. Methods: On the basis of preliminary studies and references of relevant literature about Sheng-Mai-San for myocardial ischemia, we chose three representative components (ginsenoside Rb1 (G), ruscogenin (R) and schisandrin (S)) for the optimization design studies. First, the proper proportion of the combination was explored in different myocardial ischemia mice induced by isoproterenol and pituitrin based on orthogonal design. Then, the different proportion combinations were further optimized through uniform design in a multi-model and multi-index mode. Finally, the protective effect of combination was verified in three models of myocardial ischemia injured by ischemia/reperfusion, chronic intermittent hypoxia and acute infarction. Results: The optimized combination GRS (G: 6 mg/kg, R: 0.75 mg/kg, S: 6 mg/kg) obtained by experimental screening exhibited a significant protective effect on myocardial ischemia injury, as evidenced by decreased myocardium infarct size, ameliorated histological features, decreased myocardial myeloperoxidase (MPO) and malondiadehyde (MDA), calcium overload, and decreased serum lactate dehydrogenase (LDH), creatine kinase MB isoenzyme (CK-MB), cardiac troponin I (cTn-I) activity. In addition, the interactions of three components in combination GRS were also investigated. The combination, compared to G, R and S, could significantly reduce the concentration of serum CK-MB and cTn-I, and decrease myocardial infarct size, which demonstrated the advantages of this combination for myocardial ischemia. Conclusion: Our results demonstrated that the optimized combination GRS could exert significant cardioprotection against myocardial ischemia injury with similar effect compared to Sheng Mai preparations, which might provide some pharmacological evidences for further development of new modern Chinese drug for cardiovascular diseases basing on traditional Chinese formula with affirmative therapeutic effect.

Dalbavancin is a lipoglycopeptide approved for treatment of Gram-positive infections of skin and skin-associated structures (ABSSSI). Currently, off-label use at high dosages for osteoarticular infections deserves attention. This work aimed to study the long-term plasma pharmacokinetics of dalbavancin in outpatients with ABSSSI or osteoarticular infections, treated either with one or two 1500 mg doses of dalbavancin. A liquid chromatography-tandem mass spectrometry method was used to measure total dalbavancin concentrations in plasma samples. The results were analyzed through a non-compartmental analysis (NCA). Breakpoint minimum inhibitory concentration (MIC) was used to calculate AUC/MIC and T > MIC parameters, adjusted by 93% protein binding. A total of 14 patients were enrolled, 11 with osteoarticular infection and 3 with ABSSSI. Long-term pharmacokinetics showed median T > MIC (0.125 mg/L) of 11.9 and 13.7 weeks for single and dual dose, respectively. Similarly, median AUC0-2w/MIC ratios of 20,590 and 31,366 were observed for single and dual dose regimens, respectively. No adverse events were observed, and treatment success was achieved in 12/14 patients. Failure was associated with the worst clinical conditions, bone infections, and single dose. The results of this study show that dalbavancin exposure exceeds previously suggested pharmacodynamic targets. Optimization of these targets is needed for the osteoarticular setting.

Doxorubicin is a widely-used chemotherapeutic drug, however its high toxicity poses a significant challenge for its clinical use. To address this issue, a physiologically-based pharmacokinetic (PBPK) model was implemented to quantitatively assess doxorubicin toxicity at cellular scale. Due to its unique pharmacokinetic behavior (e.g. high volume of distribution and affinity to extra-plasma tissue compartments), we proposed a modified PBPK model structure and developed the model with multispecies extrapolation to compensate for the limitation of obtaining clinical tissue data. Our model predicted the disposition of doxorubicin in multiple tissues including clinical tissue data with an overall absolute average fold error (AAFE) of 2.12. The model's performance was further validated with 8 clinical datasets in combined with intracellular doxorubicin concentration with an average AAFE of 1.98. To assess the potential cellular toxicity, toxicity levels and area under curve (AUC) were defined for different dosing regimens in toxic and non-toxic scenarios. The cellular concentrations of doxorubicin in multiple organ sites associated with commonly observed adverse effects (AEs) were simulated and calculated the AUC for quantitative assessments. Our findings supported the clinical dosing regimen of 75 mg/m2 with a 21-day interval and suggest that slow infusion and separated single high doses may lower the risk of developing AEs from a cellular level, providing valuable insights for the risk assessment of doxorubicin chemotherapy. In conclusion, our work highlights the potential of PBPK modelling to provide quantitative assessments of cellular toxicity and supports the use of clinical dosing regimens to mitigate the risk of adverse effects. [Display omitted] • Developed and validated PBPK model through interspecies extrapolation, leveraging clinical and preclinical PK data at the plasma, organ, and cellular levels. • Explored doxorubicin's cellular toxicity based on concentration and AUC, yielding fresh insights into its potential risks. • Developed the first validated PBPK model for doxorubicin, enabling accurate predictions of its distribution in diverse tissues. • Significantly influences refined doxorubicin dosing and the development of less toxic doxorubicin formulations. [ABSTRACT FROM AUTHOR]

Potency is a central parameter in pharmacological and biochemical sciences, as well as in drug discovery and development endeavors. It is however typically defined in terms only of ligand to target binding affinity also in in vivo experimentation, thus in a manner analogous to in in vitro studies. As in vivo potency is in fact a conglomerate of events involving ligand, target, and target-ligand complex processes, overlooking some of the fundamental differences between in vivo and in vitro may result in serious mispredictions of in vivo efficacious dose and exposure. The analysis presented in this paper compares potency measures derived from three model situations. Model A represents the closed in vitro system, defining target binding of a ligand when total target and ligand concentrations remain static and constant. Model B describes an open in vivo system with ligand input and clearance ( Cl ( L ) ), adding in parallel to the turnover ( k syn , k deg ) of the target. Model C further adds to the open in vivo system in Model B also the elimination of the target-ligand complex ( k e ( RL ) ) via a first-order process. We formulate corresponding equations of the equilibrium (steady-state) relationships between target and ligand, and complex and ligand for each of the three model systems and graphically illustrate the resulting simulations. These equilibrium relationships demonstrate the relative impact of target and target-ligand complex turnover, and are easier to interpret than the more commonly used ligand-, target- and complex concentration-time courses. A new potency expression, labeled L 50 , is then derived. L 50 is the ligand concentration at half-maximal target and complex concentrations and is an amalgamation of target turnover, target-ligand binding and complex elimination parameters estimated from concentration-time data. L 50 is then compared to the dissociation constant K d (target-ligand binding affinity), the conventional Black & Leff potency estimate EC 50 , and the derived Michaelis-Menten parameter K m (target-ligand binding and complex removal) across a set of literature data. It is evident from a comparison between parameters derived from in vitro vs . in vivo experiments that L 50 can be either numerically greater or smaller than the K d (or K m ) parameter, primarily depending on the ratio of k deg -to- k e ( RL ) . Contrasting the limit values of target R and target-ligand complex RL for ligand concentrations approaching infinity demonstrates that the outcome of the three models differs to a great extent. Based on the analysis we propose that a better understanding of in vivo pharmacological potency requires simultaneous assessment of the impact of its underlying determinants in the open system setting. We propose that L 50 will be a useful parameter guiding predictions of the effective concentration range, for translational purposes, and assessment of in vivo target occupancy/suppression by ligand, since it also encompasses target turnover – in turn also subject to influence by pathophysiology and drug treatment. Different compounds may have similar binding affinity for a target in vitro (same K d ), but vastly different potencies in vivo . L 50 points to what parameters need to be taken into account, and particularly that closed-system ( in vitro ) parameters should not be first choice when ranking compounds in vivo (open system). [ABSTRACT FROM AUTHOR]

Purpose Exosomes are small membrane vesicles (30–100 nm in diameter) secreted by cells into extracellular space. The present study evaluated the effect of chemotherapeutic agents on exosome production and/or release, and quantified the contribution of exosomes to intercellular drug transfer and pharmacodynamics. Methods Human cancer cells (breast MCF7, breast-to-lung metastatic LM2, ovarian A2780 and OVCAR4) were treated with paclitaxel (PTX, 2–1000 nM) or doxorubicin (DOX, 20–1000 nM) for 24–48 h. Exosomes were isolated from the culture medium of drug-treated donor cells (Donor cells) using ultra-centrifugation, and analyzed for acetylcholinesterase activity, total proteins, drug concentrations, and biological effects (cytotoxicity and anti-migration) on drug-naïve recipient cells (Recipient cells). These results were used to develop computational predictive quantitative pharmacology models. Results Cells in exponential growth phase released ~ 220 exosomes/cell in culture medium. PTX and DOX significantly promoted exosome production and/or release in a dose- and time-dependent manner, with greater effects in ovarian cancer cells than in breast cancer cells. Exosomes isolated from Donor cells contained appreciable drug levels (2–7 pmole/10 6 cells after 24 h treatment with 100–1000 nM PTX), and caused cytotoxicity and inhibited migration of Recipient cells. Quantitative pharmacology models that integrated cellular PTX pharmacokinetics with PTX pharmacodynamics successfully predicted effects of exosomes on intercellular drug transfer, cytotoxicity of PTX on Donor cells and cytotoxicity of PTX-containing exosomes on Recipient cells. Additional model simulations indicate that within clinically achievable PTX concentrations, the contribution of exosomes to active drug efflux increased with drug concentration and exceeded the p-glycoprotein efflux when the latter was saturated. Conclusions Our results indicate (a) chemotherapeutic agents stimulate exosome production or release, and (b) exosome is a mechanism of intercellular drug transfer that contributes to pharmacodynamics of neighboring cells. [ABSTRACT FROM AUTHOR]

Purpose: Bilastine is an H antagonist whose pharmacokinetics (PK) and pharmacodynamics (PD) have been resolved in adults with a therapeutic oral dose of 20 mg/day. Bilastine has favorable characteristics for use in pediatrics but the PK/PD and the optimal dose in children had yet to be clinically explored. The purpose is to: (1) Develop an ontogenic predictive model of bilastine PK linked to the PD in adults by integrating current knowledge; (2) Use the model to design a PK study in children; (3) Confirm the selected dose and the study design through the evaluation of model predictability in the first recruited children; (4) Consider for inclusion the group of younger children (< 6 years). Methods: A semi-mechanistic approach was applied to predict bilastine PK in children assuming the same PD as described in adults. The model was used to simulate the time evolution of plasma levels and wheal and flare effects after several doses and design an adaptive PK trial in children that was then confirmed using data from the first recruits by comparing observations with model predictions. Results: PK/PD simulations supported the selection of 10 mg/day in 2 to <12 year olds. Results from the first interim analysis confirmed the model predictions and design hence trial continuation. Conclusion: The model successfully predicted bilastine PK in pediatrics and optimally assisted the selection of the dose and sampling scheme for the trial in children. The selected dose was considered suitable for younger children and the forthcoming safety study in children aged 2 to <12 years. [ABSTRACT FROM AUTHOR]

Objective ： To evaluate the value of quantitative pharmacological software in the development of personalized dosage regimen of vancomycin for critically ill patients．Methods ：By combining advanced vancomycin pharmacokinetics tool and Java PK for desktop ，the initial dosage of vancomycin was calculated for the initial predicted trough concentration of vancomycin based on patient’s information ，including gender ，age ，height ，weight ，serum creatinine and disease state ．Then ，actual drug doses and predicted serum steady trough concentration （Css ，min） of vancomycin were calculated by Bayesian ．The serum concentration of vancomycin became stable after ５ half-life ．Blood samples were taken ３０ min before injection of the drug ．The fluorescence polarization immunoassay was used to determine the concentration of actual vancomycin （Css ，min） ．The correlation of actual （Css ，min） and predicted （Css ，min） was analyzed statistically by means of bivariate ．Results ：Sixty three blood samples with serum trough concentration of vancomycin were collected from sixty patients ．The predicted value of （Css ，min） was （１４ ．０２ ± ５ ．１１） μg／ml and the actual value of （Css ，min） was （１３ ．３０ ± ６ ．２２） μg／ml ．The serum trough concentration deviation value was （０ ．７３ ± ３ ．７９） μg／ml ． The predicted （Css ，min） of vancomycin was significantly related to the actual value （ r ＝０ ．７９４ ，P＜ ０ ．００１） ．The overall actual success rate was ６８ ．３％ （４３ ／６３ ） ．Conclusion ：Classic pharmacokinetic and population pharmacokinetics software could provide individualized dosage regimen of vancomycin in critically ill patients ，and display good blood drug concentration prediction ． [ABSTRACT FROM AUTHOR]

Quantitative systems pharmacology (QSP) models are an important facet of pharmaceutical and clinical research as they combine mechanistic models of physiology in health and disease with pharmacokinetics/pharmacodynamics to predict systems-level effects. The quantitative clinical pharmacology toolbox has traditionally included both mechanistic modeling and population approaches, collectively called pharmacometrics, but the current landscape requires the optimization and use of multiple models together. Here, we explore several case studies in drug development that exemplify three approaches for using QSP and pharmacometrics models together - parallel synchronization, cross-informative use, and sequential integration. While these approaches are increasingly applied in drug development, achieving a true convergence of QSP and pharmacometrics that fully exploits their synergy will require new tools and methods that enable greater technical integration, in addition to nurturing scientists with diverse modeling expertise that enable cross-discipline strategy. Extensions of existing methods used in each approach as well as additional resources including machine learning models, data-at-scale, end-to-end computation platforms, and real-time analytics will enable this convergence. [ABSTRACT FROM AUTHOR]

Objective: To explore the necessity of using quantitative pharmacology to optimize drug treatment regimens for critical patients. Methods: Through therapeutic drug monitoring, dosage adjustment, observation of efficacy, adverse drug reaction monitoring and other pharmaceutical care, clinical pharmacists cooperated with physicians in the choice of antimicrobials and dose adjustment in the treatment of one case of surgical infection after percutaneous coronary intervention (PCI) complicated with bloodstream infection and uremia, and also the results of pharmaceutical care were analyzed and evaluated. Results: The patient was confirmed to have bacteriemia with methicillin resistant Staphylococcus aureu. He showed to have poor response to initial treatment by vacomycin at a dosage of 0.5 g every 48 h and there might be infective endocarditis. So the patient was switched to have daptomycin with a loading dosage of 0.4 g and the dosage of 0.5 g every 48 h was maintained during the course of treatment. Finally, the bloodstream infection was effectively controlled. Conclusion: The active involvement of clinical pharmacists in the development and adjustment of anti-infective treatment regimens by the way of quantitative pharmacology would help to improve drug efficacy and safety in the treatment of critical patients. [ABSTRACT FROM AUTHOR]

Background: A novel translational pharmacology investigation was conducted by combining an in vitro efficacy target with mucosal tissue pharmacokinetic (PK) data and mathematical modeling to determine the number of doses required for effective human immunodeficiency virus (HIV) preexposure prophylaxis (PrEP).Methods: A PK/pharmacodynamic (PD) model was developed by measuring mucosal tissue concentrations of tenofovir, emtricitabine, their active metabolites (tenofovir diphosphate [TFVdp] and emtricitabine triphosphate [FTCtp], respectively), and competing endogenous nucleotides (dATP and dCTP) in 47 healthy women. TZM-bl and CD4(+) T cells were used to identify 90% effective concentration (EC90) ratios of TFVdp to dATP and FTCtp to dCTP (alone and in combination) for protection against HIV. Monte-Carlo simulations were then performed to identify minimally effective dosing strategies to protect lower female genital tract and colorectal tissues.Results: The colorectal TFVdp concentration was 10 times higher than that in the lower female genital tract, whereas concentrations of endogenous nucleotides were 7-11 times lower. Our model predicted that ≥98% of the population achieved protective mucosal tissue exposure by the third daily dose of tenofovir disoproxil fumarate plus emtricitabine. However, a minimum adherence to 6 of 7 doses/week (85%) was required to protect lower female genital tract tissue from HIV, while adherence to 2 of 7 doses/week (28%) was required to protect colorectal tissue.Conclusions: This model is predictive of recent PrEP trial results in which 2-3 doses/week was 75%-90% effective in men but ineffective in women. These data provide a novel approach for future PrEP investigations that can optimize clinical trial dosing strategies. [ABSTRACT FROM AUTHOR]

The first nonclinical drug development tool (DDT) advanced by the Critical Path to TB Drug Regimens (CPTR) Initiative through a regulatory review process has been endorsed by leading global regulatory authorities. DDTs with demonstrated predictive accuracy for clinical and microbiological outcomes are needed to support decision making. Regulatory endorsement of these DDTs is critical for drug developers, as it promotes confidence in their use in Investigational New Drug and New Drug Application filings. The in vitro hollow fiber system model of tuberculosis (HFS-TB) is able to recapitulate concentration-time profiles (exposure) observed in patients for single drugs and combinations, by evaluating exposure measures for the ability to kill tuberculosis in different physiologic conditions. Monte Carlo simulations make this quantitative output useful to inform susceptibility breakpoints, dosage, and optimal combination regimens in patients, and to design nonclinical experiments in animal models. The Pre-Clinical and Clinical Sciences Working Group within CPTR executed an evidencebased evaluation of the HFS-TB for predictive accuracy. This extensive effort was enabled through the collaboration of subject matter experts representing the pharmaceutical industry, academia, product development partnerships, and regulatory authorities including the Food and Drug Administration (FDA) and the European Medicines Agency (EMA). A comprehensive analysis plan following the regulatory guidance documents for DDT qualification was developed, followed by individual discussions with the FDA and the EMA. The results from the quantitative analyses were submitted to both agencies, pursuing regulatory DDT endorsement. The EMA Qualification Opinion for the HFS-TB DDT was published 26 January 2015 (available at: http://www.ema.europa.eu/ ema/index.jsp?curl=pages/regulation/document_listing/document_listing_000319.jsp). [ABSTRACT FROM AUTHOR]

Given the complexity of pharmacological challenge experiments, it is perhaps not surprising that design and analysis, and in turn interpretation and communication of results from a quantitative point of view, is often suboptimal. Here we report an inventory of common designs sampled from anti-inflammatory, respiratory and metabolic disease drug discovery studies, all of which are based on animal models of disease involving pharmacological and/or patho/physiological interaction challenges. The corresponding data are modeled and analyzed quantitatively, the merits of the respective approach discussed and inferences made with respect to future design improvements. Although our analysis is limited to these disease model examples, the challenge approach is generally applicable to the vast majority of pharmacological intervention studies. In the present five Case Studies results from pharmacodynamic effect models from different therapeutic areas were explored and analyzed according to five typical designs. Plasma exposures of test compounds were assayed by either liquid chromatography/mass spectrometry or ligand binding assays. To describe how drug intervention can regulate diverse processes, turnover models of test compound–challenger interaction, transduction processes, and biophase time courses were applied for biomarker response in eosinophil count, IL 6 response, paw-swelling, TNF α response and glucose turnover in vivo . Case Study 1 shows results from intratracheal administration of Sephadex, which is a glucocorticoid-sensitive model of airway inflammation in rats. Eosinophils in bronchoalveolar fluid were obtained at different time points via destructive sampling and then regressed by the mixed-effects modeling. A biophase function of the Sephadex time course was inferred from the modeled eosinophil time courses. In Case Study 2, a mouse model showed that the time course of cytokine-induced IL 1β challenge was altered with or without drug intervention. Anakinra reversed the IL 1β induced cytokine IL 6 response in a dose-dependent manner. This Case Study contained time courses of test compound (drug), challenger (IL 1β ) and cytokine response (IL 6 ), which resulted in high parameter precision. Case Study 3 illustrates collagen-induced arthritis progression in the rat. Swelling scores (based on severity of hind paw swelling) were used to describe arthritis progression after the challenge and the inhibitory effect of two doses of an orally administered test compound. In Case Study 4, a cynomolgus monkey model for lipopolysaccharide LPS-induced TNF α synthesis and/or release was investigated. This model provides integrated information on pharmacokinetics and in vivo potency of the test compounds. Case Study 5 contains data from an oral glucose tolerance test in rats, where the challenger is the same as the pharmacodynamic response biomarker (glucose). It is therefore convenient to model the extra input of glucose simultaneously with baseline data and during intervention of a glucose-lowering compound at different dose levels. Typically time-series analyses of challenger- and biomarker-time data are necessary if an accurate and precise estimate of the pharmacodynamic properties of a test compound is sought. Erosion of data, resulting in the single-point assessment of drug action after a challenge test, should generally be avoided. This is particularly relevant for situations where one expects time-curve shifts, tolerance/rebound, impact of disease, or hormetic concentration–response relationships to occur. [ABSTRACT FROM AUTHOR]

Despite the publication of a substantial body of preclinical and clinical information on recent recreational drugs such as 3,4-methylenedioxymethamphetamine (MDMA, ‘ecstasy’) and cathinone compounds such as mephedrone there remains a disturbing lack of consensus as to how dangerous these compounds are to the health of the individual and to society in general. This perspective proposes that use of good pharmacological practice should be mandatory in all preclinical and clinical studies. Its use will assist both translation and reverse translation of information produced in animals and clinical subjects. We propose several basic rules to be followed in all future studies. Preclinical studies should employ pharmacokinetic-pharmacodynamic integration thereby exposing animals to known or calculable drug concentrations. This will provide results relevant to pharmacology rather than toxicology and, crucially, data relevant to human drug use. Full experimental detail should be routinely provided, to allow comparison with other similar work. In clinical studies evidence should be provided that the drug under investigation has been ingested by the subjects being examined, and details given of all other drugs being ingested. Drug-drug interactions are an unavoidable confound but studies of a size that allows reliable statistical evaluation and preferably allows sub-group analysis, particularly by using meta-analysis, should help with this problem. This may require greater collaboration between investigative groups, as routinely occurs during pharmaceutical clinical trials. Other proposals include greater integration of preclinical and clinical scientists in both preclinical and clinical studies and changes in the law regarding Good Manufacturing Process (GMP) sourcing of drug for human studies. [ABSTRACT FROM AUTHOR]

Background: Children are in a continuous and dynamically changing state of growth and development. A thorough understanding of developmental pharmacokinetics (PK) and pharmacodynamics (PD) is required to optimize drug therapy in children. Data sources: Based on recent publications and the experience of our group, we present an outline on integrating pharmacometrics in pediatric clinical practice to develop evidence-based personalized pharmacotherapy. Results: Antibiotics in septic neonates and immunosuppressants in pediatric transplant recipients are provided as proof-of-concept to demonstrate the utility of pharmacometrics in clinical practice. Dosage individualization based on developmental PK-PD model has potential benefits of improving the efficacy and safety of drug therapy in children. Conclusion: The pharmacometric technique should be better developed and used in clinical practice to personalize drug therapy in children in order to decrease variability of drug exposure and associated risks of overdose or underdose. [ABSTRACT FROM AUTHOR]

Modern pharmacometrics can integrate and leverage all prior proprietary and public knowledge. Such methods can be used to scale across species or comparators, perform clinical trial simulation across alternative designs, confirm hypothesis and potentially reduce development burden, time and costs. Crucial yet typically lacking in integration is the pre-clinical stage. Prediction of PK in man, using in vitro and in vivo studies in different animal species, is increasingly well theorized but could still find wider application in drug development. The aim of the present work was to explore methods for bridging pharmacokinetic knowledge from animal species (i.v. and p.o.) and man (p.o.) into i.v. in man using the antihistamine drug bilastine as example. A model, predictive of i.v. PK in man, was developed on data from two pre-clinical species (rat and dog) and p.o. in man bilastine trials performed earlier. In the knowledge application stage, two different approaches were used to predict human plasma concentration after i.v. of bilastine: allometry (several scaling methods) and a semi-physiological method. Both approaches led to successful predictions of key i.v. PK parameters of bilastine in man. The predictive i.v. PK model was validated using later data from a clinical study of i.v. bilastine. Introduction of such knowledge in development permits proper leveraging of all emergent knowledge as well as quantification-based exploration of PK scenario, e.g. in special populations (pediatrics, renal insufficiency, comedication). In addition, the methods permit reduction or elimination and certainly optimization of learning trials, particularly those concerning alternative off-label administration routes. [ABSTRACT FROM AUTHOR]

Background: Atezolizumab has been studied in multiple indications for both pediatric and adult patient populations. Generally, clinical studies enrolling pediatric patients may not collect sufficient pharmacokinetic data to characterize the drug exposure and disposition because of operational, ethical, and logistical challenges including burden to children and blood sample volume limitations. Therefore, mechanistic modeling and simulation may serve as a tool to predict and understand the drug exposure in pediatric patients. Objective: To use mechanistic physiologically-based pharmacokinetic (PBPK) modeling to predict atezolizumab exposure at a dose of 15 mg/kg (max 1,200 mg) in pediatric patients to support dose rationalization and label recommendations. Methods: A minimal mechanistic PBPK model was used which incorporated age-dependent changes in physiology and biochemistry that are related to atezolizumab disposition such as endogenous IgG concentration and lymph flow. The PBPK model was developed using both in vitro data and clinically observed data in adults and was verified across dose levels obtained from a phase I and multiple phase III studies in both pediatric patients and adults. The verified model was then used to generate PK predictions for pediatric and adult subjects ranging from 2- to 29-year-old. Results: Individualized verification in children and in adults showed that the simulated concentrations of atezolizumab were comparable (76% within two-fold and 90% within three-fold, respectively) to the observed data with no bias for either over- or under-prediction. Applying the verified model, the predicted exposure metrics including C min , C max , and AUC tau were consistent between pediatric and adult patients with a geometric mean of pediatric exposure metrics between 0.8- to 1.25-fold of the values in adults. Conclusion: The results show that a 15 mg/kg (max 1,200 mg) atezolizumab dose administered intravenously in pediatric patients provides comparable atezolizumab exposure to a dose of 1,200 mg in adults. This suggests that a dose of 15 mg/kg will provide adequate and effective atezolizumab exposure in pediatric patients from 2- to 18-year-old., Competing Interests: Authors WH, PChan, CS, YC, BW, JJ, Pchanu are salaried employees and stockholders of Genentech, Inc. FS, KG, HJ are salaried employees and stockholders of Certara LL was a salaried employee and stockholder of Certara during the execution of the study presented in this manuscript and now is a salaried employee of Daiichi Sankyo. Author GR was employed by F Hoffmann-La Roche Ltd., (Copyright © 2022 Huang, Stader, Chan, Shemesh, Chen, Gill, Jones, Li, Rossato, Wu, Jin and Chanu.)

3,4-Methylenedioxymethamphetamine (MDMA) induces both acute adverse effects and long-term neurotoxic loss of brain 5-HT neurones in laboratory animals. However, when choosing doses, most preclinical studies have paid little attention to the pharmacokinetics of the drug in humans or animals. The recreational use of MDMA and current clinical investigations of the drug for therapeutic purposes demand better translational pharmacology to allow accurate risk assessment of its ability to induce adverse events. Recent pharmacokinetic studies on MDMA in animals and humans are reviewed and indicate that the risks following MDMA ingestion should be re-evaluated. Acute behavioural and body temperature changes result from rapid MDMA-induced monoamine release, whereas long-term neurotoxicity is primarily caused by metabolites of the drug. Therefore acute physiological changes in humans are fairly accurately mimicked in animals by appropriate dosing, although allometric dosing calculations have little value. Long-term changes require MDMA to be metabolized in a similar manner in experimental animals and humans. However, the rate of metabolism of MDMA and its major metabolites is slower in humans than rats or monkeys, potentially allowing endogenous neuroprotective mechanisms to function in a species specific manner. Furthermore acute hyperthermia in humans probably limits the chance of recreational users ingesting sufficient MDMA to produce neurotoxicity, unlike in the rat. MDMA also inhibits the major enzyme responsible for its metabolism in humans thereby also assisting in preventing neurotoxicity. These observations question whether MDMA alone produces long-term 5-HT neurotoxicity in human brain, although when taken in combination with other recreational drugs it may induce neurotoxicity. LINKED ARTICLES This article is commented on by Parrott, pp. 1518-1520 of this issue. To view this commentary visit and to view the the rebuttal by the authors (Green et al., pp. 1521-1522 of this issue) visit [ABSTRACT FROM AUTHOR]

The advancement of therapeutic monoclonal antibodies during various stages of the drug development process can be effectively streamlined when appropriate translational strategies are applied. Design of successful translational strategies for development of monoclonal antibodies should allow for understanding of the dose- and concentration-response relationships with respect to both beneficial and toxic effects from early phases of drug development. Evaluation of relevant biomarkers during early stages of drug development should facilitate the successful design of safe and effective dosing strategies. Moreover, application of quantitative pharmacology is critical for translation of exposure-response relationships early on. [ABSTRACT FROM AUTHOR]

Effective therapeutic options for Alzheimer’s disease (AD) are limited and much research is currently ongoing. The high attrition rate in drug development is a critical issue. Here, the quantitative pharmacology approach (QP-A) and model-based drug development (MBDD) provide a valuable opportunity to support early selection of the most promising compound and facilitate a fast, efficient, and rational drug development process. The aim of this analysis was to exemplify the QP-A by eventually predicting the clinical outcome of a proof-of-concept (PoC) trial of tesofensine in AD patients from two small phase IIa trials. Retrospective population pharmacokinetic/pharmacodynamic (PK/PD) modeling of tesofensine, its metabolite M1, and assessment scale-cognitive subscale data from two 4-week placebocontrolled studies in 62 mild AD patients was performed using non-linear mixed effects modeling. The final PK/PD model was used to predict data of a negative 14-week phase IIb PoC trial (430 AD patients). For the PK, one-compartment models for tesofensine and M1 with first-order absorption and elimination were sufficient. An extended Emax model including disease progression best described the PK/PD relationship using effect compartments. The placebo effect was also implemented in the final PK/PD model based on a published placebo model developed in a large AD cohort. Various internal evaluation techniques confirmed the reliability and predictive performance of the PK/PD model, which also successfully predicted the 14-week PoC data. For tesofensine, the dose concentration–effect relationship has successfully been described in mild AD patients demonstrating the supportive value of PK/PD models in QP-A/MBDD in early phases of clinical development for decision-making. [ABSTRACT FROM AUTHOR]

The First International Symposium on Quantitative Pharmacology in Drug Development and Regulatory Sciences was held this past October in Nanjing, China, marking the first time scientists from around the globe gathered to discuss topics related to quantitative pharmacology in the Far East. With the recent trend toward global drug development and clinical trials in nontraditional countries, China has been regarded by many as the next frontier for the pharmaceutical industry. Quantitative pharmacology embraces all phases of pharmaceutical research and development, providing a mechanism to bridge decision making from one phase of development to the next, and it facilitates multidisciplinary partnerships through the assembly of both data and models that describe complex biological, biopharmaceutic, and clinical settings. Efforts in China are at an early stage, but it is clear that Chinese scientists embrace the discipline and are keen to promote this methodology in the registration of new drugs in China. While challenges exist, they represent an exciting area of future collaboration. [ABSTRACT FROM AUTHOR]

HAE1, a high-affinity anti-IgE monoclonal antibody, is discussed here as a case study in the use of quantitative pharmacology in the development of a second-generation molecule. In vitro, preclinical, and clinical data from the first-generation molecule, omalizumab, were heavily leveraged in the HAE1 program. A preliminary mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model for HAE1 was developed using an existing model for omalizumab, together with in vitro binding data for HAE1 and omalizumab. When phase I data were available, the model was refined by simultaneously modeling PK/PD data from omalizumab studies with the available HAE1 phase I data. The HAE1 clinical program was based on knowledge of the quantitative relationship between a pharmacodynamic biomarker, suppression of free IgE, and clinical response (e.g., lower exacerbation rates) obtained in pivotal studies with omalizumab. A clinical trial simulation platform was developed to predict free IgE levels and clinical responses following attainment of a target free IgE level (≤10 IU/ml). The simulation platform enabled selection of four doses for the phase II dose-ranging trial by two independent methods: dose-response non-linear fitting and linear mixed modeling. Agreement between the two methods provided confidence in the doses selected. Modeling and simulation played a large role in supporting acceleration of the HAE1 program by enabling data-driven decision-making, often based on confirmation of projections and/or learning from incoming new data. [ABSTRACT FROM AUTHOR]

Translational research is generally described as the application of basic science discoveries to the treatment or prevention of disease or injury. Its value is usually determined based on the likelihood that exploratory or developmental research can yield effective therapies. While the pharmaceutical industry has evolved into a highly specialized sector engaged in translational research, the academic medical research community has similarly embraced this paradigm largely through the motivation of the National Institute of Health (NIH) via its Roadmap initiative. The Clinical and Translational Science Award (CTSA) has created opportunities for institutions which can provide the multidisciplinary environment required to engage such research. A key component of the CTSA and an element of both the NIH Roadmap and the FDA Critical Path is the bridging of bench and bedside science via quantitative pharmacologic relationships. The infrastructure of the University of Pennsylvania/Children’s Hospital of Philadelphia CTSA is highlighted relative to both research and educational objectives reliant upon quantitative pharmacology. A case study, NIH-sponsored research program exploring NK1r antagonism for the treatment NeuroAIDS is used to illustrate the application of quantitative pharmacology in a translational research paradigm. [ABSTRACT FROM AUTHOR]