Moving the needle for oncology dose optimization: A call for action

Project Optimus is a major FDA initiative aimed at ensuring dose optimization in oncology drug development, moving away from the maximum tolerated dose paradigm and prospectively characterizing dose–response for efficacy and safety for patient-focused maximization of benefit versus risk.1-3 Mitigating toxicities and enhancing overall benefit versus risk of oncology therapies necessitates dose optimization with commitment to evaluation of innovative dosing paradigms including individualized approaches, where appropriate. This requires the quantitative integration of pharmacological mechanism of action, efficacy, and safety in the context of associated population variability. The problem of dose optimization in the context of mechanism of action, cancer pathophysiology, and associated population variability sits neatly at the intersection of translational/ precision medicine and quantitative clinical pharmacology and is important to approach with a patient-focused mindset. Forums convened on the topic of oncology dose optimization largely engage scientific leaders primarily working on oncology research and development, and cancer medicine. These include workshops organized by Friends of Cancer Research (FOCR),4 American Society of Clinical Oncology (ASCO),5, 6 American Association for Cancer Research (AACR),7, 8 and the International Society of Pharmacometrics (ISoP)9 in partnership with the US Food and Drugs Administration (FDA). Of note, some of these efforts have yielded seminal publications1, 2, 10-13 and White Papers14 offering initial recommendations, including availability of a Draft FDA guidance on the topic.15 We posited that the American Society for Clinical Pharmacology and Therapeutics (ASCPT) – as a premier scientific and professional organization for clinical pharmacology and translational medicine – is optimally positioned to host a discussion of opportunities for our constituent disciplines (e.g., translational science, clinical pharmacology, pharmacometrics) to synergistically address this problem with a multi-disciplinary approach. To this end, a session was convened at the 2023 ASCPT Annual Meeting bringing together representative scientific leaders from the three scientific journals of the Society – Clinical Pharmacology and Therapeutics (CPT), Clinical and Translational Science (CTS), and CPT: Pharmacometrics and Systems Pharmacology (PSP). These scientific leaders, as at-large representatives of the disciplines of clinical pharmacology and translational medicine, were invited to bring forward their opinions and participate in a fireside chat to identify opportunities for moving the oncology dose optimization needle. This enabled engagement of a broad group of experts without requiring primary scientific or professional affiliation to the oncology therapeutic area, thereby maximizing diversity of opinion, out-of-the-box solutioning, and fresh perspectives that should help advance us beyond the current state. Ahead of the session at the Annual Meeting, a survey was launched to ASCPT members and meeting attendees to get our finger on the pulse of our Society's membership on issues faced in oncology dose optimization and provide substrate for the fireside chat with the expert panel. Herein, we present the findings from this ASCPT survey, and review the insights gained from this Annual Meeting session including recommendations for our scientific communities to join forces and drive progress. A focused survey was developed and sent out in February 2023 to meeting attendees and broader ASCPT membership on the topic of the session, which consisted of six questions that were relevant to dose optimization (Data S1). The survey was open for 3 weeks and 65 respondents participated in the survey. We were not only interested in understanding the background of survey respondents that may influence their feedback, but also various dose optimization approaches including challenges with various modalities. In response to our question about full time engagement with oncology R&D, 58% of respondents were either not engaged or only had part time engagement with oncology R&D. This suggested that survey feedback was from members with diverse backgrounds, as intended. Similarly, we were interested in understanding if strategies for dose optimization in other therapeutic areas are relevant for oncology therapies. 86% of respondents suggested that strategies from other therapeutic areas are indeed relevant to oncology. Three questions focused on approaches applied for dose optimization – one on the utility of pharmacodynamic (PD) biomarkers, another one on quantitative approaches for dose selection and finally a question on study designs for dose optimization with a focus on randomization. 92% of responses suggest that PD biomarkers are at least useful. Clinical Exposure-response modeling (57%) followed by pharmacokinetic (PK)/PD modeling (28%) are most preferred approaches for selecting doses. Of note, 62% of respondents did not consider randomized dose-ranging evaluation as necessary for dose optimization, suggesting the value of application on a case-by-case approach leveraging the totality of evidence to optimize dose (Figure 1). Given that oncology is a therapeutic area with a wide range of modalities from small molecules to cell therapies, we sought to understand the level of challenge associated with dose optimization in developing each of these modalities. Respondents noted that dose optimization for next-generation cytotoxic agents, small molecule targeted agents, and monoclonal antibodies is relatively straightforward with many historical examples to guide dose selection. However, dose optimization for antibody-drug conjugates was viewed to be moderately complex while newer modalities such as multi-specific biologics and cell therapies were considered very challenging with very few or no examples of dose optimization (Figure 2). From a translational perspective, the focus of dose optimization is to find the right dose for patients as swiftly and safely as possible, buttressed by nonclinical and clinical translational data. Translational dose optimization doesn't always have to be complex. Goldstein et al.16 describe a relatively simple concept for translational dose optimization for small molecule targeted oncology agents in the first-in-human setting. These suggestions can be implemented today. The approved doses of 25 targeted therapies were examined and the average free concentration at steady state (Css) was determined to be similar to the in vitro cell potency (half-maximal inhibitory concentration (IC50)). Furthermore, the authors propose a revised first-in-human trial design for next-generation targeted therapy in which dose cohort expansion is initiated at doses less than the maximum tolerated dose when there is evidence of clinical activity and Css exceeds a threshold informed by in vitro cell potency. Ji et al.17 describe another relatively straightforward approach to translational dose optimization in oncology. In this case, the drug is an inhibitor of Porcupine, a membrane-bound O-acyltransferase required for Wnt secretion. Wnt pathway is expressed in skin tissues; AXIN2 mRNA expression in skin is a robust and sensitive biomarker for the Wnt pathway. A predominant safety issue in this case is dysgeusia. The authors performed integrated population PK and exposure-response analyses of PD biomarker and safety data to determine the recommended dose for expansion, rather than the conventional maximum tolerated approach. More complex approaches are also possible and have great utility, particularly for complex therapeutic modalities. Weddell et al.18 describe an elegant mechanistic model that characterizes antibody drug conjugate (ADC) pharmacokinetics and tumor penetration by incorporating tumor growth inhibition via ADC binding radially across solid tumors. The model demonstrates that with low target expression, the potency of the payload should be increased. Furthermore, the model mechanistically links clinical response rates and relapse or resistance to ADC therapies, which could facilitate dose optimization. In another recent example, Susilo et al. leveraged a quantitative systems pharmacology (QSP) model of an anti-CD20/CD3 T-cell engaging bispecific antibody, mosunetuzumab, to account for different dosing regimens and inter-patient heterogeneity in the phase I study to identify biological determinants of clinical response and dose/exposure-response relationships using a novel QSP-derived digital twins approach.19 Approaches of this nature raise opportunities for multi-dimensional optimization across the dimensions of dose, patient population, and combination partner – a challenge faced routinely in oncology drug development. The value of new, innovative biomarkers in translational development is continuing to be realized. Recent examples indicate the emerging value of circulating tumor DNA (ctDNA).20, 21 The translational utility of ctDNA, cancer cell DNA found in the bloodstream, is manifold, including detecting and diagnosing cancer, guiding tumor-specific treatment, monitoring treatment and remission. In the context of dose optimization, characterizing the underlying exposure-response relationship for on-treatment ctDNA dynamics to inform definition of a clinically active dose range represents an untapped opportunity. Another important innovation has been in the area of digital health technologies such as a proposed multi-domain, digital model for capturing functional status, and health-related quality of life in oncology,22 which can be particularly relevant to realize the promise of Project Optimus aimed at dosage optimization for improved quality of life during long-term therapy. ASCPT, clinical pharmacologists, and translational scientists have a key role in collaboration on dose optimization challenges and opportunities across different stakeholders. ASCPT membership straddles a variety of stakeholders including academics, industry, regulators, and others to help drive brainstorming and consensus formation. For example, Ji et al.,23 reported on an ASCPT annual scientific meeting symposium. The authors describe a number of challenges observed before Project Optimus, including post-market dose-finding, continued use of traditional 3 + 3 designs, lack of characterization of chronic toxicity, and opportunities for adopting novel designs and testing more than one dose in phase 2/3 clinical trials. Oncology is one of the most innovative fields in science and yet there are only very few examples of value-added use of pharmacodynamic biomarkers and dose optimization. Cross-stakeholder work and Project Optimus are expected to drive the field to increased biomarker-based and model-informed solutions for oncology dose finding and optimization. In their paper “The Future of Clinical Trial Design in Oncology,” Spreafico and co-workers from the Toronto Princess Margaret Cancer Centre24 describe how therapeutic approaches in cancer drug discovery and development have shifted from traditional cytotoxic chemotherapy focused on histology-based targets to molecularly targeted and immune therapies in patient subsets stratified by biomarkers and other diagnostic precision tools. The authors argue that the classical clinical trial paradigm in oncology urgently needs to be transformed to ensure patients will benefit from this scientific revolution in a timely manner. In a wide-ranging call to action, they present a patient-centric framework for the next-generation oncology clinical trials, which maps out the journey of a trial participant as a dynamic and adaptive one continuously leveraging scientific and technological innovations to develop individualized therapeutic strategies. They conclude that “The success of next-generation clinical trials will be based on the fundamental principles of acting locally to learn globally and treating participants individually to advance the field collectively.” This speaks directly to the opportunity for clinical pharmacology to play a core role in this new paradigm, in particular with regard to dose optimization and individualization based on quantitative, model-informed approaches that integrate the totality knowledge and data of the drug, disease, and patient. An example of such an approach is QSP, which in a recent survey conducted by the ISoP was identified as an emerging key tool utilized by oncology drug developers for dose and dose regimen selection and optimization.25 A recent example was presented by Li et al.,26 who developed a mechanistic model to determine the recommended phase II dose (R2P2D) for epcoritamab, a CD3×CD20 bispecific antibody (bsAb). The authors justified this novel approach, which integrated preclinical, clinical PK, biomarker, tumor, and response data from the dose-escalation part of the phase I/II trial, on the basis that traditional dose/exposure-response modeling methods may not adequately predict the complex dose/exposure-response relationship for bsAbs. Therefore, trimer formation predicted by the mechanistic model instead of actual clinical measures was used to guide dose prediction. Along the same lines, in a paper by Chelliah and representatives from a consortium of pharmaceutical companies,27 the case is made that conventional, empirical pharmacometrics approaches do not fully capitalize on all the available biological and disease knowledge and that QSP models provide a more rational and better alternative to guide complex IO combination therapy development. Their proposal that “virtual patients” simulated by the QSP model under conditions that mimic the actual clinical trial should be added to the drug development paradigm is fully aligned with the earlier-mentioned call-to-action by Spreafico et al. outlined in Figure 2 of their publication,24 suggesting that the future of clinical trial design in oncology may already have arrived. Poorly characterized dose and schedule may lead to selection of a dose that provides more toxicity without additional efficacy, severe toxicities that require a high rate of dose reductions or premature discontinuation and may result in missed opportunity for continued benefit from the drug. To optimize benefit versus risk with a patient-focused approach, there remain significant opportunities for model-based analyses to inform dosing regimen design that may sometimes involve non-static posology, with patient response or outcome-based dose adaptation to ensure individualized dosing for maximizing benefit versus risk.28, 29 Project Optimus offers a pivotal opportunity to reform the oncology dosing paradigm using a robust quantitative clinical pharmacology framework.2, 3, 14, 30-33 By integrating a model development lifecycle, Bayesian trial designs, and a learning-and-confirming mindset across the development spectrum, this framework may be used to prospectively guide dose optimization. The model development lifecycle (Figure 3; top panel) consists of building and revising a collection of models that can be used to answer key development questions that define the drug label. A priori consideration of the quantitative pharmacologic inputs to a model can guide the design elements of a clinical trial such as establishing early data access points of pharmacokinetic and biomarker data within an open-label design. Bayesian and adaptive components can improve trial efficiency and enable rapid model updates as data emerge for end-to-end model development that utilizes the totality of evidence as it is generated.34-36 A quantitative framework to predict, interpret, and contextualize emerging data, and sometimes before it is even available through simulations of proposed outcomes, can approximate a real-time analysis. This model development lifecycle, which is both influenced by, and is influential to the design of clinical studies, becomes the model-informed drug development hypothesis within the drug development lifecycle. Contemporary early development trials in oncology have evolved to utilize Bayesian model-based and model-assisted designs. They offer seamless movement across early development through expansion cohorts that blend dose escalation with efficacy evaluation.37, 38 Introducing key optimization metrics like pharmacokinetics and pharmacodynamics can lower the risk of underdosing and integrate key intrinsic and extrinsic factors that explain inter-individual variability to reduce bias in dose determination. Several recent examples extend the dose-toxicity design to include exposure to improve the understanding of the benefit–risk relationship for a potential drug.39-44 A learning-and-confirming mindset, which is well-established in drug development, remains under-utilized in oncology. It can prospectively guide dose optimization by integrating the model development lifecycle and Bayesian trial designs in a Bayesian framework that uses the totality of data from across a development program to learn and confirm as evidence is generated.36 Figure 3 (bottom panel) illustrates this framework. Expanded dose escalation trials that are larger than a similar conventional trial (to overcome the small sample size of early phase trials and heterogeneity in tumor biology and disease that impact the ability to establish early signals of efficacy) can generate robust data to preliminarily characterize the relationships between exposure, toxicity/tolerability, and efficacy. These data and the models developed to describe the data can inform dose selection for a subsequent dose-ranging trial. The evidence may also be combined in a Bayesian framework with prior models and data, collectively defining a group of prior distributions with some elements being more informative than others based on source and quality. The prior probability distribution and emerging data collected in a dose-ranging trial can predict the posterior probability of one or more dose levels maximizing a desired benefit–risk ratio. 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