Building an adaptive dose simulation framework to aid dose and schedule selection.

Establishing a dosing regimen that maximizes clinical benefit and minimizes adverse effects for novel therapeutics is a key objective for drug developers. Finding an optimal dose and schedule can be particularly challenging for compounds with a narrow therapeutic window such as in oncology. Modeling and simulation tools can be valuable to conduct in silico evaluations of various dosing scenarios with the goal to identify those that could minimize toxicities, avoid unscheduled dose interruptions, or minimize premature discontinuations, which all could limit the potential for therapeutic benefit. In this tutorial, we present a stepwise development of an adaptive dose simulation framework that can be used for dose optimization simulations. The tutorial first describes the general workflow, followed by a technical description with basic to advanced practical examples of its implementation in mrgsolve and is concluded with examples on how to use this in decision-making around dose and schedule optimization. The adaptive simulation framework is built with pharmacokinetic, pharmacodynamic (i.e., biomarkers, activity markers, target engagement markers, efficacy markers), and safety models that include evaluations of unexplained interindividual and intraindividual variability and covariate impact, which can be replaced and expanded (e.g., combination setting, comparator setting) with user-defined models. Subsequent adaptive simulations allow investigation of the impact of starting dose, dosing intervals, and event-driven (exposure or effect) dose modifications on any end point. The resulting simulation-derived insights can be used in quantitatively proposing dose and regimens that better balance benefit and adverse effects for further evaluation, aiding dose selection discussions, and designing dose modification recommendations, among others.

Model-based meta-analysis of non-small cell lung cancer with standard of care PD-1 inhibitors and chemotherapy for early development decision making.

Single-arm cohorts/trials are often used in early phase oncology programs to support preliminary clinical activity assessments for investigational products, administered alone or in combination with standard of care (SOC) agents. Benchmarking clinical activity of those combinations against other treatments, including SOC, requires indirect comparisons against external trials, which presents challenges including cross-study differences in trial populations/other factors. To facilitate such nonrandomized comparisons, we developed a comprehensive model-based meta-analysis (MBMA) framework to quantitatively adjust for factors related to efficacy in metastatic non-small cell lung cancer (mNSCLC). Data were derived from 15 published studies assessing key programmed cell death protein-1 (PD-1) inhibitors pembrolizumab (n = 8) and nivolumab (n = 7), representing current SOC in mNSCLC. In the first stage, a mixed-effects logistic regression model for overall response rate (ORR) was developed accounting for effects of various population covariates on ORR. The ORR model results indicated an odds ratio (OR) of 1.02 for squamous versus non-squamous histology and OR of 1.20 for PD-ligand 1 tumor proportion score (TPS) per every 10% increase of TPS level. Next, a nonparametric mixed-effects model for overall survival (OS) was developed with ORR/other clinical covariates as input. Subsequently, MBMA simulations of relevant hypothetical scenarios involving single-arm trial design predicted OS hazard ratios as a function of ORR with matched patient characteristics. Findings from this MBMA and derived parameter estimates can be generally applied by the reader as a framework for interpreting efficacy data from early phase trials to support ORR-based go/no-go decisions and futility rules, illustrated through examples in this report.

Comparative analysis of PD-1 target engagement of dostarlimab and pembrolizumab in advanced solid tumors using ex vivo IL-2 stimulation data.

Dostarlimab (JEMPERLI) is an anti-programmed cell death protein-1 (PD-1) monoclonal antibody (mAb) which is approved by the US Food and Drug Administration for patients with recurrent/advanced mismatch repair-deficient solid tumors, including endometrial cancer, following progression on prior treatment, with approval based on data from the phase I GARNET trial. To support dostarlimab dose regimen recommendations, we estimated and compared the potency of dostarlimab relative to anti-PD-1 mAb pembrolizumab using both data published from the KEYNOTE-001 trial of pembrolizumab and data from the GARNET trial. PD-1 target engagement was assessed ex vivo in blood samples via a super antigen staphylococcal enterotoxin B stimulation assay and interleukin-2 (IL-2) stimulation ratios calculated for dostarlimab. A non-linear mixed-effect sigmoid maximum effect inhibitory model was fitted to dostarlimab IL-2 stimulation ratios using extracted pembrolizumab data as informative priors. The estimated half-maximal effective concentration was 1.95 µg ml-1 (95% credibility interval: 0.21-5.87) for dostarlimab and 1.59 µg ml-1 (95% confidence interval: 0.42-6.12) for pembrolizumab. These findings suggest dostarlimab and pembrolizumab to be equipotent for peripheral PD-1 suppression based on analysis of ex vivo IL-2 stimulation ratios. Accounting for a three-fold dilution between serum and tumor, a target dostarlimab trough concentration of ~54 µg ml-1 would be needed for 90% suppression in the tumor. These data support the use of dostarlimab as a potent PD-1 suppressor and the recommended dostarlimab monotherapy dose regimen of 500 mg Q3W ×4 cycles followed by 1000 mg Q6W thereafter in recurrent/advanced solid tumors.

Population pharmacokinetics and exposure-response of anti-programmed cell death protein-1 monoclonal antibody dostarlimab in advanced solid tumours.

AIM: Develop a population pharmacokinetic (PopPK) model to characterise the pharmacokinetics (PK) of anti-programmed cell death protein-1 (PD-1) antibody dostarlimab, identify covariates of clinical relevance, and investigate efficacy/safety exposure-response (ER) relationships. METHODS: A PopPK model was developed using Phase 1 GARNET (NCT02715284) trial data for dostarlimab (1, 3 or 10 mg kg-1 every 2 wk; 500 mg every 3 wk or 1000 mg every 6 wk; 500 mg every 3 wk × 4 then 1000 mg every 6 wk [recommended regimen]) serum concentrations over time. Concentration-time data were analysed using nonlinear mixed effects modelling with standard stepwise covariate modelling. ER was explored for treatment-related adverse events and overall response rate (ORR) using logistic regression. RESULTS: PopPK model/adverse event ER analyses included 546 patients (ORR ER analysis n = 362). Dostarlimab PK was well described by a 2-compartment model with time-dependent linear elimination. Time-dependent clearance decreased over time to a maximum of 14.9%. At steady state, estimated dostarlimab geometric mean coefficient of variation % clearance was 0.179 (30.2%) L d-1 ; volume of distribution was 5.3 (14.2%) L; terminal elimination half-life was 23.5 (22.4%) days. Statistically significant covariates were age, body weight, sex, time-varying albumin and alanine aminotransferase for clearance; body weight, albumin and sex for volume of distribution of the central compartment. Hepatic or renal impairment did not affect PK. There were no clinically significant ER relationships. CONCLUSION: Dostarlimab PK parameters are similar to other anti-programmed cell death protein-1 antibodies. The clinical impact of covariates on exposure was limited-to-moderate, supporting recommended dostarlimab monotherapy therapeutic dosing.

Exposure-Response Analyses for Therapeutic Dose Selection of Belantamab Mafodotin in Patients With Relapsed/Refractory Multiple Myeloma.

Belantamab mafodotin is an antibody-drug conjugate comprising a humanized anti-B-cell maturation antigen (BCMA) monoclonal antibody conjugated to monomethyl auristatin F (MMAF) via a protease-resistant maleimidocaproyl linker. Single-agent belantamab mafodotin showed clinically meaningful activity and manageable safety in patients with heavily pretreated relapsed/refractory multiple myeloma (RRMM) in the phase I DREAMM-1 and phase II DREAMM-2 studies and is approved by the US Food and Drug Administration and European Medicines Agency for RRMM treatment. To support monotherapy dose selection, the relationship between Cycle 1 exposure (derived using a population pharmacokinetic model) and clinical response (for multiple efficacy and safety end points) was explored. In DREAMM-2, efficacy end points (probability of response (PoR) and progression-free survival (PFS)) were associated with exposure in univariate evaluation; however, once disease burden factors were included in the model (e.g., baseline soluble BCMA, ß2 -microglobulin), exposure was no longer significant. Patients with higher disease burden had lower exposure. In DREAMM-1, belantamab mafodotin exposure was the only variable to correlate with PoR and PFS. Probability of corneal events (keratopathy), but not dry eye or blurred vision, was strongly associated with belantamab mafodotin exposure (DREAMM-2). Higher cys-mcMMAF maximum plasma drug concentration (Cmax ) and lower baseline platelet count were associated with increased probability of thrombocytopenia (DREAMM-1 and DREAMM -2). In general, safety end points were more strongly associated with belantamab mafodotin exposure than efficacy end points, particularly after disease factors and patient characteristics were taken into account. Overall, these findings supported the monotherapy dose recommendation of belantamab mafodotin as 2.5 mg/kg every 3 weeks in patients with RRMM who have received four or more prior therapies.

A Model-Informed Drug Discovery and Development Strategy for the Novel Glucose-Responsive Insulin MK-2640 Enabled Rapid Decision Making.

A model-informed drug discovery and development strategy played a key role in the novel glucose-responsive insulin MK-2640's early clinical development strategy and supported a novel clinical trial paradigm to assess glucose responsiveness. The development and application of in silico modeling approaches by leveraging substantial published clinical insulin pharmacokinetic-pharmacodynamic (PKPD) data and emerging preclinical and clinical data enabled rapid quantitative decision making. Learnings can be applied to define PKPD properties of novel insulins that could become therapeutically meaningful for diabetic patients.

Toward Progress in Quantitative Translational Medicine: A Call to Action.

Quantitative translational medicine (QTM) is envisioned as a multifaceted discipline that will galvanize the path from idea to medicine through quantitative translation across the discovery, development, regulatory, and utilization spectrum. Here, we summarize results of an American Society for Clinical Pharmacology and Therapeutics (ASCPT) survey on barriers relevant to the advancement of QTM and propose opportunities for its deployment. Importantly, we offer a call to action to break down these barriers through patient-centered stewardship, effective communication, cross-sector collaboration, and a modernized educational curriculum.

Clinical Evaluation of MK-2640: An Insulin Analog With Glucose-Responsive Properties.

The goal of this investigation was to examine clinical translation of glucose responsiveness of MK-2640, which is a novel insulin saccharide conjugate that can bind the insulin receptor or mannose receptor C type 1 (MRC1), the latter dependent upon glucose concentration. In a rising dose study in 36 healthy adults under euglycemic clamp conditions, rising exposures revealed saturation of MK-2640 clearance, likely due to saturation of clearance by MRC1. Potency of MK-2640 was ~25-fold reduced relative to regular human insulin. In a randomized, 2-period crossover trial in 16 subjects with type 1 diabetes mellitus to evaluate glucose-responsiveness of i.v. administered MK-2640, we were unable to demonstrate a glucose-dependent change in MK-2640 clearance, although a significant glucose-dependent augmentation of glucose infusion rate was observed. These pharmacokinetic (PK) and pharmacodynamic (PD) data provide crucial insights into next steps for developing an insulin saccharide conjugate as a clinically effective glucose-responsive insulin analog.

Model-Informed Drug Discovery and Development: Current Industry Good Practice and Regulatory Expectations and Future Perspectives.

Good practices around model-informed drug discovery and development (MID3) aim to improve the implementation, standardization, and acceptance of these approaches within drug development and regulatory review. A survey targeted to clinical pharmacology and pharmacometric colleagues across industry, the US Food and Drug Administration (FDA), and the European Medicines Agency (EMA) was conducted to understand current and future roles of MID3. The documented standards were generally affirmed as a "good match" to current industry practice and regulatory expectations, with some identified gaps that are discussed. All have seen at least a "modest" step forward in MID3 implementation associated with greater organizational awareness and share the expectation for a future wider use and impact. The priority within organizations was identified as a limitation with respect to the future of MID3. Finally, potential solutions, including a global overarching MID3 regulatory guideline, to facilitate greater acceptance by industry and regulatory decision makers are discussed.

Perspective on the State of Pharmacometrics and Systems Pharmacology Integration.

Reliance on modeling and simulation in drug discovery and development has dramatically increased over the past decade. Two disciplines at the forefront of this activity, pharmacometrics and systems pharmacology (SP), emerged independently from different fields; consequently, a perception exists that only few examples integrate these approaches. Herein, we review the state of pharmacometrics and SP integration and describe benefits of combining these approaches in a model-informed drug discovery and development framework.

Getting Innovative Therapies Faster to Patients at the Right Dose: Impact of Quantitative Pharmacology Towards First Registration and Expanding Therapeutic Use.

Quantitative pharmacology (QP) applications in translational medicine, drug-development, and therapeutic use were crowd-sourced by the ASCPT Impact and Influence initiative. Highlighted QP case studies demonstrated faster access to innovative therapies for patients through 1) rational dose selection for pivotal trials; 2) reduced trial-burden for vulnerable populations; or 3) simplified posology. Critical success factors were proactive stakeholder engagement, alignment on the value of model-informed approaches, and utilizing foundational clinical pharmacology understanding of the therapy.

Assessment of translational risk in drug research: Role of biomarker classification and mechanism-based PKPD concepts.

In 2005, Danhof and coauthors proposed a new biomarker classification in the context of the application of mechanism-based PKPD modeling. They defined the term 'biomarker' as a measure that characterizes a drug-induced response, which is on the causal path between drug administration and clinical outcome. The biomarker classification identified seven categories that provide different insights into the kinetics of drug action, such as target site distribution, target engagement, or into the impact of the drug on physiology or disease. The original biomarker classification has been further modified into a translational biomarker scheme that is used as a communication tool for drug hunting teams to guide designing translational and early clinical development plans as part of an integrated model-informed drug discovery and development strategy. It promotes a dedicated discussion on the topic of the translational relevance of biomarkers and enables efficient identification of translational gaps and opportunities. Based on the elucidated PKPD characteristics exhibited by a novel drug and the kinetics of the investigated biomarker, prospective predictions can be made for the drug response under new conditions; translating from the preclinical arena to the clinical setting, from the healthy volunteer to the patient, or from an adult to an elderly or a child. These drug response predictions provide support to decisions on appropriate next steps in the development of the drug, while keeping clear line of sight on the potential to address unmet medical need. Moreover, this framework enables a transparent translational risk assessment for drug hunting projects, and as such can underpin decisions at program and portfolio level.

The variability in beta-cell function in placebo-treated subjects with type 2 diabetes: application of the weight-HbA1c-insulin-glucose (WHIG) model.

AIM: The weight-glycosylated haemoglobin (HbA1C)-insulin-glucose (WHIG) model describes the effects of changes in weight on insulin sensitivity (IS) in newly diagnosed, obese subjects receiving placebo treatment. This model was applied to a wider population of placebo-treated subjects, to investigate factors influencing the variability in IS and ß-cell function. METHODS: The WHIG model was applied to the WHIG dataset (Study 1) and two other placebo datasets (Studies 2 and 3). Studies 2 and 3 consisted of nonobese subjects and subjects with advanced type 2 diabetes mellitus (T2DM). Body weight, fasting serum insulin (FSI), fasting plasma glucose (FPG) and HbA1c were used for nonlinear mixed-effects modelling (using NONMEM v7.2 software). Sources of interstudy variability (ISV) and potential covariates (age, gender, diabetes duration, ethnicity, compliance) were investigated. RESULTS: An ISV for baseline parameters (body weight and ß-cell function) was required. The baseline ß-cell function was significantly lower in subjects with advanced T2DM (median difference: Study 2: 15.6%, P < 0.001; Study 3: 22.7%, P < 0.001) than in subjects with newly diagnosed T2DM (Study 1). A reduction in the estimated insulin secretory response in subjects with advanced T2DM was observed but diabetes duration was not a significant covariate. CONCLUSION: The WHIG model can be used to describe the changes in weight, IS and ß-cell function in the diabetic population. IS remained relatively stable between subjects but a large ISV in ß-cell function was observed. There was a trend towards decreasing ß-cell responsiveness with diabetes duration, and further studies, incorporating subjects with a longer history of diabetes, are required.

Preclinical Experimental and Mathematical Approaches for Assessing Effective Doses of Inhaled Drugs, Using Mometasone to Support Human Dose Predictions.

BACKGROUND: Understanding the relationship between dose, lung exposure, and drug efficacy continues to be a challenging aspect of inhaled drug development. An experimental inhalation platform was developed using mometasone furoate to link rodent lung exposure to its in vivo pharmacodynamic (PD) effects. METHODS: We assessed the effect of mometasone delivered directly to the lung in two different rodent PD models of lung inflammation. The data obtained were used to develop and evaluate a mathematical model to estimate drug dissolution, transport, distribution, and efficacy, following inhaled delivery in rodents and humans. RESULTS: Mometasone directly delivered to the lung, in both LPS and Alternaria alternata rat models, resulted in dose dependent inhibition of BALf cellular inflammation. The parameters for our mathematical model were calibrated to describe the observed lung and systemic exposure profiles of mometasone in humans and in animal models. We found that physicochemical properties, such as lung fluid solubility and lipophilicity, strongly influenced compound distribution and lung retention. CONCLUSIONS: Presently, we report on a novel and sophisticated mathematical model leading to improvements in a current inhaled drug development practices by providing a quantitative understanding of the relationship between PD effects and drug concentration in lungs.

Pooled population pharmacokinetic model of imipenem in plasma and the lung epithelial lining fluid.

AIMS: Several clinical trials have confirmed the therapeutic benefit of imipenem for treatment of lung infections. There is however no knowledge of the penetration of imipenem into the lung epithelial lining fluid (ELF), the site of action relevant for lung infections. Furthermore, although the plasma pharmacokinetics (PK) of imipenem has been widely studied, most studies have been based on selected patient groups. The aim of this analysis was to characterize imipenem plasma PK across populations and to quantify imipenem ELF penetration. METHODS: A population model for imipenem plasma PK was developed using data obtained from healthy volunteers, elderly subjects and subjects with renal impairment, in order to identify predictors for inter-individual variability (IIV) of imipenem PK. Subsequently, a clinical study which measured plasma and ELF concentrations of imipenem was included in order to quantify lung penetration. RESULTS: A two compartmental model best described the plasma PK of imipenem. Creatinine clearance and body weight were included as subject characteristics predictive for IIV on clearance. Typical estimates for clearance, central and peripheral volume, and inter-compartmental clearance were 11.5 l h(-1) , 9.37 l, 6.41 l, 13.7 l h(-1) , respectively (relative standard error (RSE) <8%). The distribution of imipenem into ELF was described using a time-independent penetration coefficient of 0.44 (RSE 14%). CONCLUSION: The identified lung penetration coefficient confirms the clinical relevance of imipenem for treatment of lung infections, while the population PK model provided insights into predictors of IIV for imipenem PK and may be of relevance to support dose optimization in various subject groups.

A PK-PD model-based assessment of sugammadex effects on coagulation parameters.

Exposure-response analyses of sugammadex on activated partial thromboplastin time (APTT) and prothrombin time international normalized ratio (PT(INR)) were performed using data from two clinical trials in which subjects were co-treated with anti-coagulants, providing a framework to predict these responses in surgical patients on thromboprophylactic doses of low molecular weight or unfractionated heparin. Sugammadex-mediated increases in APTT and PT(INR) were described with a direct effect model, and this relationship was similar in the presence or absence of anti-coagulant therapy in either healthy volunteers or surgical patients. In surgical patients on thromboprophylactic therapy, model-based predictions showed 13.1% and 22.3% increases in respectively APTT and PT(INR) within 30min after administration of 16mg/kg sugammadex. These increases remain below thresholds seen following treatment with standard anti-coagulant therapy and were predicted to be short-lived paralleling the rapid decline in sugammadex plasma concentrations.

Application of a systems pharmacology model for translational prediction of hERG-mediated QTc prolongation.

Drug-induced QTc interval prolongation (Δ QTc) is a main surrogate for proarrhythmic risk assessment. A higher in vivo than in vitro potency for hERG-mediated QTc prolongation has been suggested. Also, in vivo between-species and patient populations' sensitivity to drug-induced QTc prolongation seems to differ. Here, a systems pharmacology model integrating preclinical in vitro (hERG binding) and in vivo (conscious dog Δ QTc) data of three hERG blockers (dofetilide, sotalol, moxifloxacin) was applied (1) to compare the operational efficacy of the three drugs in vivo and (2) to quantify dog-human differences in sensitivity to drug-induced QTc prolongation (for dofetilide only). Scaling parameters for translational in vivo extrapolation of drug effects were derived based on the assumption of system-specific myocardial ion channel densities and transduction of ion channel block: the operational efficacy (transduction of hERG block) in dogs was drug specific (1-19% hERG block corresponded to ≥10 msec Δ QTc). System-specific maximal achievable Δ QTc was estimated to 28% from baseline in both dog and human, while %hERG block leading to half-maximal effects was 58% lower in human, suggesting a higher contribution of hERG-mediated potassium current to cardiac repolarization. These results suggest that differences in sensitivity to drug-induced QTc prolongation may be well explained by drug- and system-specific differences in operational efficacy (transduction of hERG block), consistent with experimental reports. The proposed scaling approach may thus assist the translational risk assessment of QTc prolongation in different species and patient populations, if mediated by the hERG channel.

Structure-Based Prediction of Anti-infective Drug Concentrations in the Human Lung Epithelial Lining Fluid.

PURPOSE: Obtaining pharmacologically relevant exposure levels of antibiotics in the epithelial lining fluid (ELF) is of critical importance to ensure optimal treatment of lung infections. Our objectives were to develop a model for the prediction of the ELF-plasma concentration ratio (EPR) of antibiotics based on their chemical structure descriptors (CSDs). METHODS: EPR data was obtained by aggregating ELF and plasma concentrations from historical clinical studies investigating antibiotics and associated agents. An elastic net regularized regression model was used to predict EPRs based on a large number of CSDs. The model was tuned using leave-one-drug-out cross validation, and the predictions were further evaluated using a test dataset. RESULTS: EPR data of 56 unique compounds was included. A high degree of variability in EPRs both between- and within drugs was apparent. No trends related to study design or pharmacokinetic factors could be identified. The model predicted 80% of the within-drug variability (R(2) WDV) and 78.6% of drugs were within 3-fold difference from the observations. Key CSDs were related to molecular size and lipophilicity. When predicting EPRs for a test dataset the R(2) WDV was 75%. CONCLUSIONS: This model is of relevance to inform dose selection and optimization during antibiotic drug development of agents targeting lung infections.