Development of a Joint Tumor Size-Overall Survival Modeling and Simulation Framework Supporting Oncology Development Decision-Making.

Tumor size-overall survival (TS-OS) models can support decision-making in oncology drug development by predicting long-term OS based on TS data from early data cuts and baseline patient factors. The current work describes the development of a TS-OS framework capable of predicting OS across a variety of treatment modalities and mechanisms of action in patients with non-small cell lung cancer from seven clinical studies. The presented framework jointly models TS with a bi-exponential Stein model and OS with an accelerated failure time log-normal survival model.

Tutorial on Conditional Simulations With a Tumor Size-Overall Survival Model to Support Oncology Drug Development.

The gold standard for regulatory approval in oncology is overall survival (OS). Because OS data are initially limited, early drug development decisions are often based on early efficacy endpoints, such as objective response rate and progression-free survival. Tumor size (TS)-OS models provide a framework to support decision-making on potential late-stage success based on early readouts, through leveraging TS data with limited follow-up and treatment-agnostic TS-OS link functions, to predict longer-term OS.

Integrated Longitudinal Population Dose-Hemoglobin Response of Daprodustat Following Dose Titration in Patients With Anemia in Chronic Kidney Disease.

Daprodustat, a novel oral hypoxia-inducible factor prolyl hydroxylase inhibitor is approved in the United States for the treatment of anemia due to chronic kidney disease (CKD) in adults receiving dialysis for at least 4 months. Pharmacodynamic dose-hemoglobin (Dose-Hgb) models were developed as daprodustat progressed through development. To support global phase III development, a dose-titration algorithm, guided by simulations from the initial Dose-Hgb model based on phase II clinical data, was implemented.

How debunking biases in research and development decisions could lead to more equitable healthcare?

Decades of research have demonstrated that a variety of cognitive biases can affect our judgment and ability to make rational decisions in personal and professional environments. The lengthy, risky, and costly nature of pharmaceutical research and development (R&D) makes it vulnerable to biased decision-making. Moreover, cognitive biases can play a role in regulatory and clinical decision-making, the latter impacting diagnostic and treatment decisions in the therapeutic use of medicines.

Belantamab mafodotin concentration-QTc relationships in patients with relapsed or refractory multiple myeloma from the DREAMM-1 and -2 studies.

Aims: To evaluate relationships between plasma concentrations of belantamab mafodotin, total monoclonal antibody, and its payload and changes in electrocardiogram (ECG) parameters in patients with relapsed or refractory multiple myeloma from the DREAMM-1 and DREAMM-2 studies.

Methods: Hysteresis plots and linear regression analyses of pharmacokinetic (PK) analyte (belantamab mafodotin, total monoclonal antibody, and cytotoxic cysteine-maleimidocaproyl monomethyl auristatin F payload) concentrations vs. time-matched ECG parameters (absolute/change from baseline in QT interval corrected for RR interval [QTc/ΔQTc] and QT interval corrected for heart rate by Fridericia's formula [QTcF/ΔQTcF]) were performed.

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.

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.

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 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.

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.

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.

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.

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.

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.

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

Drug-induced QTc interval prolongation ( 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.

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 β-cell function.

Methods: The WHIG model was applied to the WHIG dataset (Study 1) and two other placebo datasets (Studies 2 and 3).

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.

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.