Assessing the Impact of Tissue Target Concentration Data on Uncertainty in In Vivo Target Coverage Predictions

Asia-Inclusive Clinical Research and Development Enabled by Translational Science and Quantitative Clinical Pharmacology: Toward a Culture That Challenges the Status Quo

Access lag to innovative therapies in Asian populations continues to present a challenge to global health. Recent progressive changes in the global regulatory landscape, including newer guidelines, are enabling simultaneous global drug development and near-simultaneous global drug registration. The International Conference on Harmonization (ICH) E17 guideline outlines general principles for the design and analysis of multiregional clinical trials (MRCTs). We posit that translational research and quantitative clinical pharmacology tools are core enablers for Asia-inclusive global drug development aligned with ICH E17 principles. Assessment of ethnic sensitivity should be initiated early in the development lifecycle to inform the need for, and extent of, Asian phase I ethno-bridging data. Relevant ethno-bridging data may be generated as standalone Asian phase I trials, as part of Western First-In-Human trials, or under accelerated development settings as a lead-in phase in an MRCT. Quantitative understanding of human clearance mechanisms and pharmacogenetic factors is vital to forecasting ethnic sensitivity in drug exposure using physiologically-based pharmacokinetic models. Stratification factors to control heterogeneity in MRCTs can be identified by reverse translational research incorporating pharmacometric disease models and model-based meta-analyses. Because epidemiological variations can extend to the molecular level, quantitative systems pharmacology models may be useful in forecasting how molecular variation in therapeutic targets or pathway proteins across populations might impact treatment outcomes. Through prospective evaluation of conservation in drug- and disease-related intrinsic and extrinsic factors, a pooled East Asian region can be implemented in Asia-inclusive MRCTs to maximize efficiency in substantiating evidence of benefit-risk for the region at-large with a Totality of Evidence approach.

Dose Finding in Oncology: What is Impeding Coming of Age?

After a drug molecule enters clinical trials, there are primarily three levers to enhance probability of success: patient selection, dose selection and choice of combination agents. Of these, dose selection remains an under-appreciated aspect in oncology drug development despite numerous peer-reviewed publications. Here, we share practical challenges faced by the biopharmaceutical industry that reduce the willingness to invest in dose finding for oncology drugs. First, randomized dose finding admittedly slows down clinical development. To reduce the size of dose finding study, trend in exposure vs. tumor-size analysis can be assessed, instead of a statistical test for non-inferiority between multiple doses. Second, investment in testing a lower dose when benefit-risk at the higher dose is sufficient for regulatory approval (i.e., efficacy at the higher dose is better than standard of care and safety is acceptable) is perceived as low priority. Changing regulatory landscape must be considered to optimize dose in pre-marketing setting as post-marketing changes in dose can be commercially costly. Third, the risk of exposing patients to subtherapeutic exposures with a lower dose should be assessed scientifically instead of assuming a monotonic relationship between dose and efficacy. Only the doses which are expected to be at the plateau of dose/exposure-response curve should be investigated in Phase 1b/2. Overall, changing the perceptions that have been impeding investment in dose finding in oncology requires pragmatic discourse among biopharmaceutical industry, regulatory agencies and academia. These perceptions should also not deter dose finding for recently emerging modalities, including BITEs and CART cell therapies.

Characterizing the pharmacokinetics and biodistribution of therapeutic proteins: an industry white paper

Characterization of the pharmacokinetics (PK) and biodistribution of therapeutic proteins (TPs) is a hot topic within the pharmaceutical industry, particularly with an ever-increasing catalog of novel modality TPs. Here, we review the current practices, and provide a summary of extensive cross-company discussions as well as a survey completed by International Consortium for Innovation and Quality (IQ consortium) members on this theme. A wide variety of in vitro, in vivo and in silico techniques are currently used to assess PK and biodistribution of TPs, and we discuss the relevance of these from an industry perspective, focusing on PK/PD understanding at the preclinical stage of development, and translation to human. We consider that the 'traditional in vivo biodistribution study' is becoming insufficient as a standalone tool, and thorough characterization of the interaction of the TP with its target(s), target biology, and off-target interactions at a microscopic scale are key to understand the overall biodistribution at a full-body scale. Our summary of the current challenges and our recommendations to address these issues could provide insight into the implementation of best practices in this area of drug development, and continued cross-company collaboration will be of tremendous value. Significance Statement The Innovation & Quality Consortium (IQ) Translational and ADME Sciences Leadership Group (TALG) working group for the ADME of therapeutic proteins evaluates the current practices, recent advances, and challenges in characterizing the PK and biodistribution of therapeutic proteins during drug development, and proposes recommendations to address these issues. Incorporating the in vitro, in vivo and in silico approaches discussed herein may provide a pragmatic framework to increase early understanding of PK/PD relationships, and aid translational modelling for first-in-human dose predictions.

A Mechanistic Site-Of-Action Model: A Tool for Informing Right Target, Right Compound, And Right Dose for Therapeutic Antagonistic Antibody Programs

Quantitative modeling is increasingly utilized in the drug discovery and development process, from the initial stages of target selection, through clinical studies. The modeling can provide guidance on three major questions–is this the right target, what are the right compound properties, and what is the right dose for moving the best possible candidate forward. In this manuscript, we present a site-of-action modeling framework which we apply to monoclonal antibodies against soluble targets. We give a comprehensive overview of how we construct the model and how we parametrize it and include several examples of how to apply this framework for answering the questions postulated above. The utilities and limitations of this approach are discussed.

Bispecific antibodies: A guide to model informed drug discovery and development

Affinity (KD) optimization of monoclonal antibodies is one of the factors that impacts the stoichiometric binding and the corresponding efficacy of a drug. This impacts the dose and the dosing regimen, making the optimum KD a critical component of drug discovery and development. Its importance is further enhanced for bispecific antibodies, where affinity of the drug needs to be optimized with respect to two targets. Mathematical modeling can have critical impact on lead compound optimization. Here we build on previous work of using mathematical models to facilitate lead compound selection, expanding analysis from two membrane bound targets to soluble targets as well. Our analysis reveals the importance of three factors for lead compound optimization: drug affinity to both targets, target turnover rates, and target distribution throughout the body. We describe a method that leverages this information to help make early stage decisions on whether to optimize affinity, and if so, which arm of the bispecific should be optimized. We apply the proposed approach to a variety of scenarios and illustrate the ability to make improved decisions in each case. We integrate results to develop a bispecific antibody KD optimization guide that can be used to improve resource allocation for lead compound selection, accelerating advancement of better compounds. We conclude with a discussion of possible ways to assess the necessary levels of target engagement for affecting disease as part of an integrative approach for model-informed drug discovery and development.

Biomeasures and mechanistic modeling highlight PK/PD risks for a monoclonal antibody targeting Fn14 in kidney disease

Discovery of the upregulation of fibroblast growth factor-inducible-14 (Fn14) receptor following tissue injury has prompted investigation into biotherapeutic targeting of the Fn14 receptor for the treatment of conditions such as chronic kidney diseases. In the development of monoclonal antibody (mAb) therapeutics, there is an increasing trend to use biomeasures combined with mechanistic pharmacokinetic/pharmacodynamic (PK/PD) modeling to enable decision making in early discovery. With the aim of guiding preclinical efforts on designing an antibody with optimized properties, we developed a mechanistic site-of-action (SoA) PK/PD model for human application. This model incorporates experimental biomeasures, including concentration of soluble Fn14 (sFn14) in human plasma and membrane Fn14 (mFn14) in human kidney tissue, and turnover rate of human sFn14. Pulse-chase studies using stable isotope-labeled amino acids and mass spectrometry indicated the sFn14 half-life to be approximately 5 hours in healthy volunteers. The biomeasures (concentration, turnover) of sFn14 in plasma reveals a significant hurdle in designing an antibody against Fn14 with desired characteristics. The projected dose (>1 mg/kg/wk for 90% target coverage) derived from the human PK/PD model revealed potential high and frequent dosing requirements under certain conditions. The PK/PD model suggested a unique bell-shaped relationship between target coverage and antibody affinity for anti-Fn14 mAb, which could be applied to direct the antibody engineering towards an optimized affinity. This investigation highlighted potential applications, including assessment of PK/PD risks during early target validation, human dose prediction and drug candidate optimization.

Optimal Affinity of a Monoclonal Antibody: Guiding Principles Using Mechanistic Modeling

Affinity optimization of monoclonal antibodies (mAbs) is essential for developing drug candidates with the highest likelihood of clinical success; however, a quantitative approach for setting affinity requirements is often lacking. In this study, we computationally analyzed the in vivo mAb-target binding kinetics to delineate general principles for defining optimal equilibrium dissociation constant ([Formula: see text]) of mAbs against soluble and membrane-bound targets. Our analysis shows that in general [Formula: see text] to achieve 90% coverage for a soluble target is one tenth of its baseline concentration ([Formula: see text]), and is independent of the dosing interval, target turnover rate or the presence of competing ligands. For membrane-bound internalizing targets, it is equal to the ratio of internalization rate of mAb-target complex and association rate constant ([Formula: see text]). In cases where soluble and membrane-bound forms of the target co-exist, [Formula: see text] lies within a range determined by the internalization rate ([Formula: see text]) of the mAb-membrane target complex and the ratio of baseline concentrations of soluble and membrane-bound forms ([Formula: see text]). Finally, to demonstrate practical application of these general rules, we collected target expression and turnover data to project [Formula: see text] for a number of marketed mAbs against soluble (TNFα, RANKL, and VEGF) and membrane-bound targets (CD20, EGFR, and HER2).