Challenges and Opportunities in Dose Finding in Oncology and Immuno‐oncology

Today's cancer therapy has made substantial progress. Recent success in immuno-oncology (IO) and combination therapies provide opportunities to enhance activity in the broader population with less drug resistance.1-3 However, these advances also raise challenges in oncology drug development. At the 2017 American Society of Clinical Pharmacology and Therapeutics (ASCPT) Annual Meeting, these issues were addressed in a symposium with case studies illustrating challenges and opportunities. The presentations and discussion are summarized in this review. It has been increasingly acknowledged that the conventional maximum tolerated dose (MTD) approach used in oncology drug development may not be ideal, and improving dose selection is needed.4, 5 Historically, the MTD approach has dominated the oncology dose-finding paradigm. Its origin lies in development of cytotoxic agents based on the underlying assumption that the higher the dose, the greater the likelihood of efficacy and toxicity.6, 7 Dose-limiting toxicities (DLTs) are predefined in phase I dose escalation trials and, based on DLT criteria, dose is escalated until MTD is reached. Over recent decades, a variety of novel molecular modalities in target and immune therapies have become available that have challenged the traditional MTD-based dose selection paradigm. Using MTD as the only dose for late-stage development could be a major contributor to failures of past oncology development paradigms, especially since it results in a large number of dose reductions or discontinuations due to toxicity.8 Therefore, finding the right dose and schedule that best balances risk and benefit is the right direction to shift toward. Despite the need to improve dose finding in oncology, it is highly challenging due to heterogeneity of the disease, complexity of the biology, high variability of drug response, narrow therapeutic window, development of drug resistance, limitation in study design due to severity of the disease, and urgency to deliver effective treatments to patients. Moreover, oncology clinical trials may not be clearly defined by phases or well controlled. Depending on the novelty of the mechanism, the level of medical need and the promising anticancer activity of a new agent, a phase I study could evolve and grow into a registration trial. For example, the first-in-human (FIH) trail of the IO drug pembrolizumab, a monoclonal antibody blocking the interaction between programmed cell death-1 (PD-1) receptor and its ligands, served as the primary evidence supporting the initial accelerated approval of its use in melanoma.9 On the other hand, such an opportunity of seamless and accelerated development could present challenges. For instance, clinical data sets may be complex (as they may not have been designed to meet registration needs) or insufficiently powered when analyzed using conventional analysis tools. Moreover, recent success in IO therapy has raised additional challenges such as difficulty to translate from preclinical models and surrogate molecules, complexity of immune systems and their multifaceted impacts on tumors, and heterogeneity of tumors and their microenvironment. Hence, identifying opportunities to develop novel approaches to tackle challenges is key to the success of oncology drug development. At the ASCPT 2014 Annual Meeting, the MTD paradigm was challenged and expectations to improve dose selection in oncology were discussed.10 During the ASCPT 2017 Annual Meeting, we reviewed what have been accomplished in the oncology dose-finding paradigm at a symposium: “Finding the Right Dose in the Right Patients for Oncology and Immuno-oncology: Are We There Yet and How Have Quantitative Pharmacology, Translational and Precision Medicine Been Utilized?”.11 We shared our experience and commentary in challenges and opportunities in oncology and IO dose finding from the perspectives of pharmaceutical industry, clinical practice, and regulatory authority. The opportunities of utilizing modeling and simulation (M&S) during both early- and late-phase oncology drug development were advocated. Multidisciplinary collaboration that integrates quantitative and experimental sciences and translational and precision medicine was emphasized. New direction and advances, especially methods and applications in a broad spectrum of cancer therapy, were also discussed. Current challenges in defining the right dose for targeted therapies include failing to find an optimal dose during clinical development and, as a consequence, dose finding continues in postmarketing trials. An example is cabozantinib, which was approved by the US Food and Drug Administration (FDA) for metastatic medullary thyroid cancer (MTC) at a dose of 140 mg administered daily. The approved dose encountered dose reductions in 79% of patients in the pivotal phase III study and resulted in 16% of patients discontinuing due to toxicity. Subsequent studies of cabozantinib, utilizing a revised formulation, have been conducted at a much lower dose of 60 mg.12, 13 Roda et al. reported that dose reductions were prevalent for 34 recently approved targeted agents.14 Seven of these approved agents required dose reductions in more than 50% of patients. This presents a major challenge where the approved dose results in dose reductions in a large number of patients. Many additional oncology drug development challenges still exist, such as defining a tolerable dose for targeted therapies, whether phase I studies are able to identify key toxicity of these agents, and the need for optimization for dosing schedules and drug combinations. In MTD determination, the “3+3” approach is widely used in oncology trial design but may not offer the best design. In a review of published articles from 181 evaluable phase I clinical trials, 96% of them utilized a “3+3” design or its variation.15 More study design options, such as model-based Bayesian approaches, could be considered and explored in oncology clinical trials. The optimal study design in a clinical trial should be evaluated based on the totality of many factors, including scientific basis, end points, sample size, cancer type, development speed, and feasibility in clinical operation. In addition, a large effort is needed to educate clinical teams on the utility of different study designs in drug development.16 Another challenge in identifying the right dose in a phase I study is the lack of full characterization of chronic toxicity, since typical MTD determination methodologies reflect only Cycle 1 toxicity. This could misrepresent the safety profile for the cancer therapeutic agents as they are administered chronically over multiple cycles, and many long-term safety events occur much later than in Cycle 1. Additionally, treatment interruptions and dose reductions continue to happen beyond Cycle 1.17 For some targeted therapies, toxicities are delayed and assessment of a tolerable dose in DLT assessment during Cycle 1 is not always feasible. This may result in identification of a dose that is tolerable in Cycle 1 but requires reduced doses or discontinuations in a large number of patients in subsequent cycles due to delayed toxicities, resulting in patients unable to derive meaningful benefit from treatment.16 Approaches have been published where a phase Ib dose expansion cohort can be better designed to inform the recommended phase II dose (RP2D).14 The proposal includes identifying a dose tolerable in 12–20 patients with longer observations (at least in two cycles), which results in dose reductions in less than 30% of patients. Jardim et al. supported the view that challenges of appropriate dose selection from phase I to phase III are reflective of an inadequate number of patients in phase I.18 Some novel approaches and dosing paradigms are emerging and are worthy of consideration in gaining efficiency in oncology drug development. When higher drug exposure is demonstrated to provide added benefit of efficacy to patients, within-patient dose escalation strategies have been incorporated as part of clinical development, allowing selection of individualized doses for patients to derive maximal efficacy at the highest tolerable dose possible. Such an approach was adopted and successfully implemented in the drug development of axitinib, a tyrosine kinase inhibitor of vascular endothelial growth factor (VEGFR)-1, VEGFR-2, and VEGFR-3.19 Wages and Tait described novel designs for targeted agents by conducting seamless phase I/II designs that account for both toxicity and efficacy. 20 The method they proposed is a bivariate extension of the continual reassessment method (CRM), combining features of the CRM and order restricted inference. For combination trials, the Clinical Trial Design (CTD) Task Force of the National Cancer Institute (NCI) Investigational Drug Steering Committee recommended selecting dose regimens based on a biological or pharmacological rationale supported by clinical and preclinical data, taking into account potential pharmacokinetic (PK) and pharmacodynamic (PD) interactions.21 Alternative trial designs such as including controls in phase I trials and leveraging mathematical modeling and a Bayesian approach was also suggested. Dose optimization exercise is very much a balancing act in improving efficacy while keeping safety manageable. A major impediment to selection of an optimized dose in oncology lies in a lack of availability of clinical data from multiple dose levels in phase II/III studies, where primary clinical efficacy and safety are assessed. This could limit the ability to conduct robust exposure–response (ER) analyses of long-term clinical safety as well as efficacy that can help in arriving at the optimized dose(s) or regimen. Recognizing this challenge, establishing a range of phase II doses rather than studying a single phase II dose, as in the current RP2D paradigm, has been recommended.22 There are also multiple recent attempts of testing more than one dose level/regimen in phase II/III studies to maximize the dose optimization opportunity in oncology and IO.23, 24 Due to the life-threatening nature of the disease, oncology clinical trials are often complex and data interpretation is challenging. However, this also offers opportunity of integrating data from different sources such as preclinical data, biomarkers, response end points, multiple trials, and competitors. It also requires multidisciplinary collaboration in pharmaceutical Research and Development. Used proactively and properly, M&S can be a powerful tool to quantitatively inform oncology drug development. Different M&S approaches could be used in order to tackle the primary challenges encountered during different phases of development (Figure 1). Along with the learning and confirming cycles during clinical development,25 models are continuously modified based on data available at the time to inform the next stage of development. Data across molecules can also be integrated to build platform models, such as disease progression, prediction of outcome by early end points, literature meta-analysis, and quantitative and system pharmacology (QSP) models, to inform the development of molecules acting on the same pathway or in the same indication. Prior to an investigational new drug (IND) entering clinical trials, M&S focuses on FIH dose projection and predicting efficacious or biologically effective doses in humans. The primary approaches include PK/PD, physiologically-based PK (PBPK), and QSP models. Preclinical and competitor data, if available, are used to build the models. During early-phase clinical development (phase I/II), M&S informs selection of recommended dose for expansion (RDE) and RP2D. It can be used to evaluate the ER relationship and therapeutic window, characterize the time course of biological response and interpatient variability, and identify biomarkers that correlate with pathway modulation. PK/PD and ER models based on clinical data in phase I and II trials are often used and preclinical data can also be leveraged. During late-phase clinical development (phase III/IV), M&S could be used to further characterize the therapeutic window and the effects of intrinsic and extrinsic factors, evaluate the clinical utility index (CUI), confirm and justify dose selected, and characterize clinical pharmacology properties to inform the drug label. Population PK, PK/PD, ER, and disease progression models are often used to incorporate all the clinical data available. In addition, PBPK models can be used to support clinical pharmacology characterization. Depending on the stage of the development, the right M&S approach(es) should be selected to address the question(s) at hand. Even though quantitative models have been widely used in many therapeutic areas, their utility and impact in oncology and especially in IO are still evolving. Reviewing recent new drug application (NDA) and biologic license application (BLA) documents revealed that the impact of M&S on oncology dose decision varies. For instance, ER analysis of safety for axitinib was used to support dose titration schemes by the sponsor and was accepted by the FDA.19 In some cases, ER analysis of efficacy and safety was conducted to support dose justification and show ER The include pathway and inhibitor of kinase 1 and and the ER analyses were based on data from one dose level in the registration it on whether patients with exposure may have a lack of efficacy and whether patients with high exposure may have a higher safety risk the exposure range at a dose the proposed dose for all patients. In other cases, a postmarketing was to further the For example, ER analysis of safety was conducted by the sponsor for a tyrosine kinase that with additional activity growth factor receptor tyrosine kinase and Dose selection was based on preclinical and phase II data, but a postmarketing trial was required to doses regimens to the toxicity but the right dose in oncology drug development, proactively M&S in early clinical trials is This an opportunity to the right dose range to inform the design of registration trials, that data are to robust analysis that could support dose justification at the Due to in sample and heterogeneity of population at this efficacy data of an investigational oncology drug are and at this Therefore, should be made to pathway disease data from the phase I trial. This multidisciplinary collaboration between and clinical teams to proactively develop a data could be in dose finding for IO the response may be delayed or by clinical data dose selection data could also be in early-phase clinical development. example of using M&S to inform dose selection in early-phase development is a inhibitor in clinical where clinical PK, and safety data from the phase I dose escalation were analyzed to inform ER analyses were conducted for a and the on these the therapeutic window was Population PK modeling and simulation were conducted to PK of different dose integrating the therapeutic window and PK, a dose that balances response and safety was selected for the phase I expansion Another example is a that where PK/PD modeling was used to inform RP2D in patients with chronic The level of is a molecular response that with efficacy in patients. A PK/PD was to the of The for disease was based on the from the PK/PD PK were and a dose that was to exposure the for disease in of patients. demonstrated in both data is to robust PK/PD modeling in early clinical trials. and the multidisciplinary is that the right data analysis and are selected, and the right study design is implemented to the right data to appropriate In addition, due to the and seamless progression of oncology clinical trials, the analyses should be and in in order to impact dose selection in the next stage of clinical trials. For with high in exposure of drug at a dose data could inform dose selection in early clinical trials. In the described the at of the selected dose of and is the of in cell for However, for molecules with or at the of may be to In from preclinical to clinical is challenging in oncology and Many should be such as the in and target and drug Hence, the right clinical studies early clinical data, and leveraging data are all in early-phase clinical development. M&S has been widely in late-phase clinical development to confirm the right dosing for a broad dose in and characterize the effects of intrinsic and extrinsic at this stage on clinical such as primary efficacy end and major A modeling platform based on data from phase II/III trials can many further be to inform other in the with the same of in combination a monoclonal antibody that programmed cell death-1 1 is an example in IO that M&S has to late-phase clinical development. on the pivotal study with mg in or metastatic patients, ER analyses relationship for response and relationship for both events of and events of This efficacy be with doses higher than mg and safety with lower with the that only the mg dose level was explored in study with other data, these ER analyses support the dose of mg analyses were conducted in cancer based on the phase II trial models have been proposed to growth based on the of of target the in A modeling with as growth or in from as a for has been in for a variety of treatments of different However, for the recent IO therapies, the of response to treatment to be different with other with IO are with delayed and an initial in or of new In addition, IO therapies may to more long-term as by more substantial at The of such a modeling for IO therapies was evaluated based on data from the study in patients with In the in the and at with more initial in patients, and in patients. A and number of metastatic and to was This could well the in as well as of patients with the of such an oncology modeling in as the has potential as a model-based and an early end to evaluate efficacy in clinical trials. This approach is further evaluated and for broader application in both the early and phases of IO development. Recent in oncology and IO have to many treatment available to patients with This promising for patients also to more opportunity and to dosing for these treatment and combinations. However, it as a to on optimization additional challenges of a of new treatment with and on these the of and Oncology with the American of Cancer Research recently conducted a of dose-finding on dose optimization in oncology and The the to oncology drug development for kinase and new approaches including the model-based and dose-finding trials, the of multiple doses that have potential for effects as to selecting the MTD as the only dose in phase III trials, a and early approach to identifying ER of safety and efficacy as to ER analyses for safety and efficacy conducted only as an exercise to regulatory at the time of The was conducted in with the to provide an to the best of dose finding and dose selection in oncology for kinase There were presentations from industry, and Recent case studies were illustrating the impact of not the relationship and sources of exposure variability on the profile and regulatory Some were also selection of optimal dose and dosing schedule by with collaboration development. of the of was that is a need to have and urgency in that teams are at the same data and its and when new Another from this was that the not “3+3” designs for dose-finding studies and are very to other model-based dose escalation study The was by a which was in The discussion was to include large molecules, including IO with the discussion on efficacy. Data were at the use of the biological level based on data of receptor and pharmacological activity to doses for large such as dose escalation and of 1 are utilized to the exposure to patients at dose levels that are not to be biologically strategies on the lower of dose to doses while of many at the lower end of the where be of However, selection of an optimal dose for efficacy still requires analyses based on PK/PD modeling and the conduct of ER A was recently in 2017 in that on combination therapy, especially the combination of targeted therapy, and other IO agents with immune to in and dose selection and novel end that can benefit were the of an for from industry, and regulatory across different scientific to recent and challenges with anticancer across and across scientific should be the direction to This of in a is for and oncology drug development to more and better treatment to cancer patients. There are also in ER analyses that can be to better support dose For instance, patients well on study may many dose reductions that result in lower On the other hand, patients from the study early with clinical have higher as they may not many dose This could the interpretation of the ER lower exposure has better efficacy. should be in in response between the and highest exposure due to rather than due to the levels on which data are Therefore, it is that modeling of such data account for such potential or The for ER analysis are especially for when is only one dose level of and disease has been as for the need to study more than one the limitation of ER analysis based on one dose level and a potential ER relationship due to for have been approaches have been explored to address the in ER such as and better characterize the ER relationship and support dose it is more to more than one dose level in oncology phase Ib dose expansion phase II Dose is often in early-phase clinical studies by the sample and of early clinical efficacy end as and However, it is also many in late-phase clinical studies as phase by as well to effective treatment approved for The need for studying more than one dose level in pivotal trials on the therapeutic window and of the ER relationship for the of models early clinical end as response, with efficacy end as could be an effective of the between early and and maximize the of ER and dose optimization as early as possible. the right dose in the right patients is a in oncology and IO drug development. Current challenges to opportunities to novel approaches such as better trial design and M&S to maximize drug development success as well as of M&S methodologies to of efficacy to can help in optimal dose selection based on efficacy end drug development requires and a to drug development may only of the but not the is for a of data, that not have of We have to and in a in the of the of treatment for patients to derive maximal efficacy while keeping events as as possible. We that of current perspectives designs and approaches that can incorporate key PK and data, and when to dose selection can in anticancer and the and to the and reviewed the from National of The described are of the and not the of the US Food and Drug Administration or the US At the time of the symposium at the ASCPT was an of the US at the time of the was longer by the US is an and of is an and of is an and of has a or in and and from and was a with of at the time of the ASCPT and during the