Enhancing value of clinical pharmacodynamics in oncology drug development: An alliance between quantitative pharmacology and translational science

Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development

Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.

Exposure-response analyses for the MET inhibitor tepotinib including patients in the pivotal VISION trial: support for dosage recommendations

Purpose

Tepotinib is a highly selective MET inhibitor approved for treatment of non-small cell lung cancer (NSCLC) harboring METex14 skipping alterations. Analyses presented herein evaluated the relationship between tepotinib exposure, and efficacy and safety outcomes.

Methods

Exposure–efficacy analyses included data from an ongoing phase 2 study (VISION) investigating 500 mg/day tepotinib in NSCLC harboring METex14 skipping alterations. Efficacy endpoints included objective response, duration of response, and progression-free survival. Exposure–safety analyses included data from VISION, plus four completed studies in advanced solid tumors/hepatocellular carcinoma (30–1400 mg). Safety endpoints included edema, serum albumin, creatinine, amylase, lipase, alanine aminotransferase, aspartate aminotransferase, and QT interval corrected using Fridericia’s method (QTcF).

Results

Tepotinib exhibited flat exposure–efficacy relationships for all endpoints within the exposure range observed with 500 mg/day. Tepotinib also exhibited flat exposure–safety relationships for all endpoints within the exposure range observed with 30–1400 mg doses. Edema is the most frequently reported adverse event and the most frequent cause of tepotinib dose reductions and interruptions; however, the effect plateaued at low exposures. Concentration-QTc analyses using data from 30 to 1400 mg tepotinib resulted in the upper bounds of the 90% confidence interval being less than 10 ms for the mean exposures at the therapeutic (500 mg) and supratherapeutic (1000 mg) doses.

Conclusions

These analyses provide important quantitative pharmacologic support for benefit/risk assessment of the 500 mg/day dosage of tepotinib as being appropriate for the treatment of NSCLC harboring METex14 skipping alterations.

Registration Numbers

NCT01014936, NCT01832506, NCT01988493, NCT02115373, NCT02864992.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-022-04441-3.

Pivotal Dose of Pembrolizumab: A Dose-Finding Strategy for Immuno-Oncology

Despite numerous publications emphasizing the value of dose finding, drug development in oncology is dominated by the mindset that higher dose provides higher efficacy. Examples of dose finding implemented by biopharmaceutical firms can change this mindset. The purpose of this article is to outline a pragmatic dose selection strategy for immuno-oncology (IO) and other targeted monoclonal antibodies (mAbs). The approach was implemented for pembrolizumab. Selecting a recommended phase II dose (RP2D) with a novel mechanism of action is often challenging due to uncertain relationships between pharmacodynamics measurements and clinical end points. Additionally, phase I efficacy and safety data are generally inadequate for RP2D selection for IO mAbs. Here, the RP2D was estimated based on phase I (clinical study KN001 A and A2) pharmacokinetics data as the dose required for target saturation, which represents a surrogate for maximal pharmacological effect for antagonist mAbs. Due to limitations associated with collecting and analyzing tumor biopsies, characterizing intratumoral target engagement (TE) is challenging. To overcome this gap, a physiologically-based pharmacokinetic model was implemented to predict intratumoral TE. As tumors are spatially heterogeneous, TE was predicted in well-vascularized and poorly vascularized tumor regions. Additionally, impact of differences in target expression, for example, due to interindividual variability and cancer type, was simulated. Simulations showed that 200 mg every 3 weeks can achieve ≥ 90% TE in clinically relevant scenarios, resulting in the recommendation of 200 mg every 3 weeks as the RP2D. Randomized dose comparison studies (KN001 B2 and D) showing similar efficacy over a fivefold dose/exposure range confirmed the RP2D as the pivotal dose.

Current standards and ethical landscape of engineered tissues-3D bioprinting perspective

Tissue engineering is an evolving multi-disciplinary field with cutting-edge technologies and innovative scientific perceptions that promise functional regeneration of damaged tissues/organs. Tissue engineered medical products (TEMPs) are biomaterial-cell products or a cell-drug combination which is injected, implanted or topically applied in the course of a therapeutic or diagnostic procedure. Current tissue engineering strategies aim at 3D printing/bioprinting that uses cells and polymers to construct living tissues/organs in a layer-by-layer fashion with high 3D precision. However, unlike conventional drugs or therapeutics, TEMPs and 3D bioprinted tissues are novel therapeutics and need different regulatory protocols for clinical trials and commercialization processes. Therefore, it is essential to understand the complexity of raw materials, cellular components, and manufacturing procedures to establish standards that can help to translate these products from bench to bedside. These complexities are reflected in the regulations and standards that are globally in practice to prevent any compromise or undue risks to patients. This review comprehensively describes the current legislations, standards for TEMPs with a special emphasis on 3D bioprinted tissues. Based on these overviews, challenges in the clinical translation of TEMPs & 3D bioprinted tissues/organs along with their ethical concerns and future perspectives are discussed.

Dose Optimization for Anticancer Drug Combinations: Maximizing Therapeutic Index via Clinical Exposure-Toxicity/Preclinical Exposure-Efficacy Modeling

PURPOSE

Recommended phase II dose (RP2D) determination for combination therapy regimens is a constrained optimization problem of maximizing antitumor activity within the constraint of clinical tolerability to provide a wide therapeutic index. A methodology for addressing this problem was developed and tested using clinical and preclinical data from combinations of the investigational drugs TAK-117, a PI3Kα inhibitor, and TAK-228, a TORC1/2 dual inhibitor.

EXPERIMENTAL DESIGN

Utilizing free fraction-corrected average concentrations, [Formula: see text] and [Formula: see text], which are the primary pharmacokinetic predictors of single-agent preclinical antitumor activity, a preclinical exposure-efficacy surface was characterized, allowing for nonlinear interactions between growth rate inhibition of the agents on a MDA-MB-361 cell line xenograft model. Logistic regression was used to generate an exposure-effect surface for [Formula: see text] and [Formula: see text] versus clinical toxicity outcomes [experiencing a dose-limiting toxicity (DLT)] in single-agent and combination dose-escalation studies. A maximum tolerated exposure curve was defined at which DLT probability was 25%; predicted antitumor activity along this curve was used to determine optimal RP2D.

RESULTS

The toxicity constraint curve determined from early clinical data predicted that any clinically tolerable combination was unlikely to result in greater antitumor activity than either single-agent TAK-117 or TAK-228 administered at their respective MTDs. Similar results were obtained with 10 other cell lines, with one agent or the other predicted to outperform the combination.

CONCLUSIONS

This methodology represents a general, principled way of evaluating and selecting optimal RP2D combinations in oncology. The methodology will be retested upon availability of clinical data from TAK-117/TAK-228 combination phase II studies.See related commentary by Mayawala et al., p. 6564.

Quantitative Systems Pharmacology: An Exemplar Model-Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development

Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium‐glucose cotransporter‐2 inhibitor drug class, and one enabling rational selection of immuno‐oncology drug combinations.

Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology

Following the approval, in recent years, of the first immune checkpoint inhibitor, there has been an explosion in the development of immuno-modulating pharmacological modalities for the treatment of various cancers. From the discovery phase to late-stage clinical testing and regulatory approval, challenges in the development of immuno-oncology (IO) drugs are multi-fold and complex. In the preclinical setting, the multiplicity of potential drug targets around immune checkpoints, the growing list of immuno-modulatory molecular and cellular forces in the tumor microenvironment-with additional opportunities for IO drug targets, the emergence of exploratory biomarkers, and the unleashed potential of modality combinations all have necessitated the development of quantitative, mechanistically-oriented systems models which incorporate key biology and patho-physiology aspects of immuno-oncology and the pharmacokinetics of IO-modulating agents. In the clinical setting, the qualification of surrogate biomarkers predictive of IO treatment efficacy or outcome, and the corresponding optimization of IO trial design have become major challenges. This mini-review focuses on the evolution and state-of-the-art of quantitative systems models describing the tumor vs. immune system interplay, and their merging with quantitative pharmacology models of IO-modulating agents, as companion tools to support the addressing of these challenges.

Clinical Pharmacology of Ixazomib: The First Oral Proteasome Inhibitor

Ixazomib, the first oral proteasome inhibitor, is approved in combination with lenalidomide and dexamethasone for the treatment of patients with multiple myeloma (MM) who have received at least one prior therapy. Ixazomib is a selective, potent, and reversible inhibitor of the 20S proteasome, and preferentially binds to and inhibits the β5 chymotrypsin-like proteolytic site. Ixazomib absorption is rapid, with a median time to reach maximum plasma concentration of approximately 1 h post-dose. Ixazomib pharmacokinetics (PK) are adequately described by a three-compartment model (terminal half-life of 9.5 days) with first-order linear absorption (oral bioavailability of 58%). Plasma exposures of ixazomib increase in a dose-proportional manner. A high-fat meal decreases both the rate and extent of ixazomib absorption, supporting administration on an empty stomach. Population PK analyses demonstrated that no dose adjustment is required based on age, body size/weight, race, sex, mild-to-moderate renal impairment, or mild hepatic impairment. Results from dedicated studies indicate that a reduced starting dose (from 4 to 3 mg) is appropriate for patients with severe renal impairment, end-stage renal disease requiring dialysis, or moderate-to-severe hepatic impairment. Non-cytochrome P450 (CYP)-mediated metabolism appears to be the major clearance mechanism for ixazomib. Drug-drug interaction studies have shown no meaningful effects of strong inhibitors of CYP3A on ixazomib PK; however, the strong inducer rifampin caused a clinically relevant reduction in ixazomib exposure, supporting the recommendation to avoid concomitant administration of ixazomib with strong CYP3A inducers. Exposure-response analyses of data from the phase III TOURMALINE-MM1 registrational study demonstrate a favorable benefit-risk profile for the approved dose and regimen of weekly ixazomib 4 mg on days 1, 8, and 15 of each 28-day cycle.

Accelerating Drug Development in Pediatric Oncology With the Clinical Pharmacology Storehouse

Pediatric drug development is a challenging process due to the rarity of the population, the need to meet regulatory requirements across the globe, the associated uncertainty in extrapolating data from adults, the paucity of validated biomarkers, and the lack of systematic testing of drugs in pediatric patients. In oncology, pediatric drug development has additional challenges that have historically delayed availability of safe and effective medicines for children. In particular, the traditional approach to pediatric oncology drug development involves conducting phase 1 studies in children once the drug has been characterized and in some cases approved for use in adults. The objective of this article is to describe clinical pharmacology factors that influence pediatric oncology trial design and execution and to highlight efficient approaches for designing and expediting oncology drug development in children. The topics highlighted in this article include (1) study design considerations, (2) updated dosing approaches, (3) ways to overcome the significant biopharmaceutical challenges unique to the oncology pediatric population, and (4) use of data analysis strategies for extrapolating data from adults, with case studies. Finally, suggestions for ways to use clinical pharmacology approaches to accelerate pediatric oncology drug development are provided.

Pharmacometric Applications and Challenges in the Development of Therapeutic Antibodies in Immuno-Oncology

Purpose of review

Monoclonal antibodies targeting key checkpoints in immune stimulatory pathways have over the last years become the mainstay of cancer immunotherapy. This article provides a brief review of the application and key impact of pharmacometrics and quantitative clinical pharmacology approaches in the development of these novel biologics.

Recent findings

The clinical development and selection of optimal dosing regimens for monoclonal antibodies used in immune-oncology has been facilitated by an extensive application of pharmacometric approaches to characterize the exposure-response relationship for major efficacy and safety endpoints. These analysis techniques were applied for the anti CTLA-4 antibody ipilimumab, as well as the anti PD1/PD-L1 antibodies nivolumab, pembrolizumab, avelumab, atezolizumab and durvalumab. The utilization of quantitative clinical pharmacology, including model-based analyses, did not only support the identification of efficacious doses with acceptable safety limits, but was also able to address complicating challenges such as time- and response-dependent changes in antibody clearance as observed for most compounds.

Summary

A widespread and systematic application of pharmacometric approaches has provided key aspects in elucidating, interpreting and integrating preclinical, biochemical and clinical data in support of the development of safe and efficacious dosing regimens of monoclonal antibodies used in immuno-oncology, thereby facilitating the clinical use of this promising new class of biologics in cancer patients with unmet medical needs.

Process Improvement for Maximized Therapeutic Innovation Outcome

Deconvoluting key biological mechanisms forms the framework for therapeutic discovery. Strategies that enable effective translation of those insights along the development and regulatory path ultimately drive validated clinical application in patients and populations. Accordingly, parity in What vs. How we transform novel mechanistic insights into therapeutic paradigms is essential in achieving success. Aligning molecular discovery with innovations in structures and processes along the discovery-development-regulation-utilization continuum maximizes the return on public and private investments for next-generation solutions in managing health and disease.

Bedside to Bench: Integrating Quantitative Clinical Pharmacology and Reverse Translation to Optimize Drug Development

With so much emphasis on reducing attrition and becoming more efficient in the delivery of healthcare, there are many opportunities to leverage existing clinical data in drug development and to foster the practice of reverse translation. The application of quantitative approaches to convert clinical trial and real‐world data to knowledge will continue to drive innovation. Herein we discuss recent examples of reverse translation and consider future opportunities to capture critical clinical knowledge to inform decision‐making in drug development.

Managing Innovation to Maximize Value Along the Discovery-Translation-Application Continuum

Success in pharmaceutical development led to a record 51 drugs approved in the past year, surpassing every previous year since 1950. Technology innovation enabled identification and exploitation of increasingly precise disease targets ensuring next generation diagnostic and therapeutic products for patient management. The expanding biopharmaceutical portfolio stands, however, in contradistinction to the unsustainable costs that reflect remarkable challenges of clinical development programs. This annual Therapeutic Innovations issue juxtaposes advances in translating molecular breakthroughs into transformative therapies with essential considerations for lowering attrition and improving the cost-effectiveness of the drug-development paradigm. Realizing the discovery-translation-application continuum mandates a congruent approval, adoption, and access triad.