Sandra A.G. Visser

Implementation of Quantitative and Systems Pharmacology in Large Pharma

Quantitative and systems pharmacology concepts and tools are the foundation of the model‐informed drug development paradigm at Merck for integrating knowledge, enabling decisions, and enhancing submissions. Rigorous prioritization of modeling and simulation activities has enabled key drug development decisions and led to a high return on investment through significant cost avoidance. Critical factors for the successful implementation, examples on impact on decision making with associated return of investment, and drivers for continued success are discussed.

5.38 – Mechanism-Based Pharmacokinetic–Pharmacodynamic Modeling for the Prediction of In Vivo Drug Concentration–Effect Relationships – Application in Drug Candidate Selection and Lead Optimization

In recent years pharmacokinetic/pharmacodynamic (PK/PD) modeling has developed as an independent scientific discipline, aiming at the prediction of the time course of drug effect in vivo. PK/PD modeling is already widely applied in drug development for optimization of the dosing and delivery of novel drugs in phase II/III clinical trials. In the meantime however PK/PD modeling is increasingly also applied in preclinical investigations. Here the objective is the prediction of the PK/PD properties of novel drugs in humans on the basis of information from in vitro bioassays and in vivo animal studies with the aim of improving lead optimization and drug candidate selection. The pertinent PK/PD information is subsequently used to optimize the design of early clinical trials. PK/PD modeling is biological systems analysis, whereby the time course of the drug effect is described mathematically on the basis of a set of drug specific and biological system specific parameters. Drug specific parameters (i.e., receptor affinity, intrinsic efficacy) characterize the interaction of the drug with the biological system and can in principle be estimated in in vitro bioassays. In contrast system specific parameters characterize the functioning of the integral biological system and can only be estimated in in vivo investigations. Mechanism-based PK/PD models contain specific expressions for processes on the causal path between drug administration and effect. These include (1) target site distribution, (2) target binding and activation, (3) transduction, and (4) the effect of homeostatic feedback mechanisms. Thereby effects on (5) disease processes are also considered. Mechanism-based PK/PD modeling relies on the use of biomarkers and pharmacodynamic endpoints, which characterize in a strictly quantitative manner processes on the causal path. In this chapter a general overview of the concepts of mechanism-based PK/PD modeling is presented. Subsequently the utility of PK/PD modeling in drug design and lead optimization is illustrated on the basis of recent research on the PK/PD correlations of adenosine A1 receptor agonists, μ opioid receptor agonists, GABAA receptor agonists, serotonin 5HT1A receptor agonists, and drug-induced QT interval prolongation. In the discussion of these examples the emphasis is on the prediction, in strictly quantitative manner, of in vivo drug effects in humans on the basis of information from (high-throughput) in vitro bioassays and in vivo animal studies.

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

PurposeObtaining 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).MethodsEPR 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.ResultsEPR 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 (R2WDV) 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 R2WDV was 75%.ConclusionsThis model is of relevance to inform dose selection and optimization during antibiotic drug development of agents targeting lung infections.

Inter-study variability of preclinical in vivo safety studies and translational exposure-QTc relationships--a PKPD meta-analysis.

Preclinical cardiovascular safety studies (CVS) have been compared between facilities with respect to their sensitivity to detect drug‐induced QTc prolongation (ΔQTc). Little is known about the consistency of quantitative ΔQTc predictions that are relevant for translation to humans.

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

Drug‐induced QTc interval prolongation (ΔQTc) 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 ΔQTc) 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 ΔQTc). System‐specific maximal achievable ΔQTc 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.

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.

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.

Pharmacometrics in Cardiovascular Safety

Approximately one third of all drug discontinuation from preclinical discovery to postapproval stage is caused by drug safety, with a large contribution of cardiovascular (CV) safety findings. Moreover, drug-induced QT prolongation and proarrhythmic liabilities have been the main reasons for labeling restrictions and drug withdrawals after approval. Pharmacometric (model-based) tools have become increasingly beneficial to assess CV liabilities, as they allow predictions under new circumstances (new dosing regimen or response in the alternative patient populations), and extrapolations across different systems (e.g., from in vitro or in vivo to clinical). This model-based analysis is particularly important for the pharmaceutical industry as it facilitates selection and progression of the best compounds at early stages, and understanding of the right dose and schedule that is safe to patients via the right clinical study design in clinical development. This chapter provides an overview on how pharmacometrics is used in drug industry to quantify the risk of CV liabilities, with the main focus on QT interval.

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.

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.