Oncology Drug Development Research Articles

Flexible seamless 2-in-1 design with sample size adaptation

ABSTRACT The 2-in-1 design is becoming popular in oncology drug development, with the flexibility in using different endpoints at different decision time. Based on the observed interim data, sponsors can choose to seamlessly advance a small phase 2 trial to a full-scale confirmatory phase 3 trial with a pre-determined maximum sample size or remain in a phase 2 trial. While this approach may increase efficiency in drug development, it is rigid and requires a pre-specified fixed sample size. In this paper, we propose a flexible 2-in-1 design with sample size adaptation, while retaining the advantage of allowing an intermediate endpoint for interim decision-making. The proposed design reflects the needs of the recent FDA’s Project FrontRunner initiative, which encourages the use of an earlier surrogate endpoint to potentially support accelerated approval with conversion to standard approval with long-term endpoints from the same randomized study. Additionally, we identify the interim decision cut-off to allow a conventional test procedure at the final analysis. Extensive simulation studies showed that the proposed design requires much a smaller sample size and shorter timeline than the simple 2-in-1 design, while achieving similar power. We present a case study in multiple myeloma to demonstrate the benefits of the proposed design.

Abstract 4723: Target engagement sheds light on difference in drug efficacy in breast cancer cell lines

Abstract Breast cancer is a serious disease that affects millions of women worldwide, and 700,000 people succumb to the disease annually. Breast cancers are divided into several functional subgroups based on molecular characteristics and growth pattern: Luminal A and B, HER2 positive (HER2+) and triple negative. The subgroups are important from a clinical perspective as they partially guide treatment strategies. In this study we wanted to investigate the difference in target engagement with four drugs indicated for treatment of different types of breast cancer: Alpelisib, Neratinib, Vinorelbine, and Docetaxel. Five different human breast cancer cell lines were included, representing Luminal A (MCF-7, T-47D), HER2+ (SK-BR-3, BT-474), and triple negative (BT-20). We used CETSA® (CEllular Thermal Shift Assay) coupled to mass spectrometry detection to assess proteome wide changes in protein thermal stability in intact breast cancer cell lines. CETSA is unique as a target engagement detection modality as it allows for target engagement assessment in intact cells with unaltered drug molecules. Also, as biology is intact in live cells it is possible to follow compound induced downstream signaling or phenotypic events that result in changed thermal stability of proteins. In addition to assessing protein thermal stability with CETSA, which is a short treatment of 1 h, we also subjected cells in culture to prolonged drug exposures, ≤72 h, in order to measure viability. This was done to correlate early changes in protein thermal stability to the viability phenotype that manifested later. In general, responses both in protein thermal stability and cell viability was heterogenous among the cell lines. The most homogenous response in viability was observed for the Phosphatidyl Inositol 3’ kinase inhibitor Alpelisib, which reduced viability to 20 - 40% in all cell lines. In contrast, partial resistance was shown to the other drugs by at least one of the cell lines. For Alpelisib, the amount and type of proteins that were thermally shifted correlated with the effect on viability. The covalent HER2 inhibitor Neratinib was most effective in inhibiting viability in HER2+ cells and proteins of the HER2 pathway were thermally shifted in these cells, which was not observed for the other cell lines. Viability of the other cell lines were also affected by Neratinib, but only at higher concentrations. Both microtubule inhibitors induced thermal stabilization of tubulins, but that did not always translate into efficacy in the viability assay. However, sensitive cell lines showed thermal shifts of tubulins at lower concentrations of compound. In oncology drug development it is important that effects on viability are driven by on-target efficacy and not by off-target effects. Here we show the benefit of connecting knowledge of target engagement and phenotypic readouts; what targets are engaged, at which concentrations, and what is the corresponding phenotype. Citation Format: Tomas Friman, Tuomas Tolvanen, Merve Kacal, Victoria Brehmer, Stina Lundgren, Laurence Arnold, Daniel Martinez Molina. Target engagement sheds light on difference in drug efficacy in breast cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4723.

Abstract 3544: OncoKB™, MSK’s precision oncology knowledge base: 2023 updates

Abstract OncoKBTM is Memorial Sloan Kettering Cancer Center’s (MSK) FDA-recognized precision oncology knowledge base that contains detailed, evidence-based information about the oncogenic effect and therapeutic implications of individual somatic mutations and structural alterations present in patient tumors. Since its public release in 2016, OncoKBTM has expanded to include annotation for >7,500 alterations in ~820 cancer-associated genes. OncoKB supports variant interpretation by the cBioPortal for Cancer Genomics, is used to annotate >15,000 MSK patient sequencing reports annually and its data is publicly available through the website (www.oncokb.org). Programmatic access to OncoKBTM data via its web-based API is freely available to users in an academic setting while users from commercial and hospital settings require a fee-based license. OncoKBTM utilizes its Therapeutic Levels of Evidence system to classify variants based on their tumor type-specific sensitivity or resistance to matched standard care or investigational targeted therapies. To date, OncoKB includes 49 Level 1 genes as well as MSI-H and TMB-H (included in the FDA drug label), 24 Level 2 genes (included in professional guidelines), 35 Level 3A genes (predictive of drug response in well-powered clinical studies), 27 Level 4 genes (predictive of drug response based on compelling biological evidence), and 11 R1/R2 resistance genes. In 2023, OncoKBTM captured the following notable changes in precision oncology drug development: Level 1 annotation of ESR1 ligand-binding domain mutations in breast cancer and promotion from Level 2 to Level 1 of ERBB2 amplification in colorectal cancer following the FDA drug approvals of elacestrant and tucatinib + trastuzumab, respectively. Additionally, KRAS G12C became a Level 2 biomarker in pancreatic and colon cancers with the listing of adagrasib and sotorasib as systemic therapy options for KRAS G12C-mutant disease in the NCCN pancreatic and colon cancer guidelines. IDH1/2 mutations in low grade glioma were annotated as Level 3A based on compelling clinical evidence demonstrating response to the IDH-specific inhibitor vorasidenib. Lastly, noting emerging data with the KRAS G12X-specific inhibitor, RMC-6236, OncoKBTM included all alleles at KRAS position G12 as Level 4. In sum, six novel clinically actionable biomarkers (all Level 1) and 14 follow-on precision oncology therapies for existing leveled biomarkers were added to OncoKBTM in the past year. OncoKBTM’s current focus includes coverage of additional cancer-associated genes, annotation of germline alterations and incorporation of OncoKBTM data into an electronic health record system. Citation Format: Sarah P. Suehnholz, Moriah Nissan, Hongxin Zhang, Ritika Kundra, Calvin Lu, Amanda Dhaneshwar, Nicole Fernandez, Benjamin Preiser, Maria E. Arcila, Marc Ladanyi, Michael F. Berger, Aijazuddin Syed, A. Rose Brannon, Ross Levine, Ahmet Dogan, Alexander Drilon, David B. Solit, Nikolaus Schultz, Debyani Chakravarty. OncoKB™, MSK’s precision oncology knowledge base: 2023 updates [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3544.

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.

Oral docetaxel plus encequidar – A pharmacokinetic model and evaluation against IV docetaxel

The development of optimized dosing regimens plays a crucial role in oncology drug development. This study focused on the population pharmacokinetic modelling and simulation of docetaxel, comparing the pharmacokinetic exposure of oral docetaxel plus encequidar (oDox + E) with the standard of care intravenous (IV) docetaxel regimen. The aim was to evaluate the feasibility of oDox + E as a potential alternative to IV docetaxel. The article demonstrates an approach which aligns with the FDA’s Project Optimus which aims to improve oncology drug development through model informed drug development (MIDD). The key question answered by this study was whether a feasible regimen of oDox + E existed. The purpose of this question was to provide an early GO / NO-GO decision point to guide drug development and improve development efficiency. Methods: A stepwise approach was employed to develop a population pharmacokinetic model for total and unbound docetaxel plasma concentrations after IV docetaxel and oDox + E administration. Simulations were performed from the final model to assess the probability of target attainment (PTA) for different oDox + E dose regimens (including multiple dose regimens) in relation to IV docetaxel using AUC over effective concentration (AUCOEC) metric across a range of effective concentrations (EC). A Go / No-Go framework was defined—the first part of the framework assessed whether a feasible oDox + E regimen existed (i.e., a PTA ≥ 80%), and the second part defined the conditions to proceed with a Go decision. Results: The overall population pharmacokinetic model consisted of a 3-compartment model with linear elimination, constant bioavailability, constant binding mechanics, and a combined error model. Simulations revealed that single dose oDox + E regimens did not achieve a PTA greater than 80%. However, two- and three-dose regimens at 600 mg achieved PTAs exceeding 80% for certain EC levels. Conclusion: The study demonstrates the benefits of MIDD using oDox + E as a motivating example. A population pharmacokinetic model was developed for the total and unbound concentration in plasma of docetaxel after administration of IV docetaxel and oDox + E. The model was used to simulate oDox + E dose regimens which were compared to the current standard of care IV docetaxel regimen. A GO / NO-GO framework was applied to determine whether oDox + E should progress to the next phase of drug development and whether any conditions should apply. A two or three-dose regimen of oDox + E at 600 mg was able to achieve non-inferior pharmacokinetic exposure to current standard of care IV docetaxel in simulations. A Conditional GO decision was made based on this result and further quantification of the “effective concentration” would improve the ability to optimise the dose regimen.

On the use of RWD in support of regulatory submission in drug development

ABSTRACT For the approval of a drug product, the United States Food and Drug Administration requires substantial evidence (SE) regarding effectiveness and safety of the test drug to be provided. In recent years, the use of real-world data in support of regulatory submission of pharmaceutical development has received much attention, and real-world evidence (RWE) is treated as complementary to SE by evaluating the real-world performance of the test treatment. In this article, we start by summarizing current regulatory perspectives on drug evaluation and some potential challenges in using RWE. To test for superiority in co-primary endpoints, a two-stage hybrid RCT/RWS adaptive design that combines randomized control trial for providing SE and real-world study for generating RWE is proposed. We use superiority in effectiveness and non-inferiority in safety as an example to illustrate how to implement this design. Numerical studies have shown that the proposed design has merits in reducing the required sample size compared with traditional co-primary endpoint tests while maintaining statistical power and controlling type I error inflation. The proposed design can be implemented in drug development considering co-primary endpoints, especially for oncology and rare disease drug development.

Japan’s contribution to an ongoing global pediatric cancer clinical trial: The experience of the National Cancer Center Hospital (NCCH) in Tokyo

Compared to Europe and the United States, where the development of pediatric oncology drugs is mandated by law, Japan hosts fewer clinical trials. This can lead to a significant drug lag, especially in the case of treatments for pediatric solid tumors. Notably, regulatory authorities and the government have initiated discussions on strategies to mitigate Japan’s drug lag problem in pediatric oncology. Over the past decade, we have actively sought opportunities to participate in international collaborative clinical trials through Japan’s involvement in ACCELERATE and its predecessor organizations. This approach has aimed to address the issue above, coinciding with the ongoing centralization of medical care and advancements in genomic medicine, among other systems that support pediatric cancer care in Japan. Inspired by the ACCELERATE initiatives and in response to patient advocacy groups’ requests for new pediatric oncology drugs, Japan established its inaugural pediatric oncology support platform in 2021, the National Childhood Cancer Consortium (N3C). N3C comprises patient advocacy groups, industries, and academia. This work outlines the current status of pediatric drug development in Japan and highlights the initial successful experience of NCCH’s participation in a global clinical trial through ACCELERATE resulting in rapid preparation and patient recruitment. Participation of Asian countries including Japan in global collaborative clinical trials may benefit both pharmaceutical companies and the participating countries partnering to advance the development of pediatric oncology drugs.

Challenges and opportunities in oncology drug development and clinical research in China

As one of the world's most populous countries, China bears a heavy burden and a broad spectrum of cancers, including unique types, providing a unique environment for drug research and development. In recent years, China has leapt forward in oncology drug development and clinical trials, presenting new opportunities and challenges.

Pediatric oncology drug development and dosage optimization.

Oncology drug discovery and development has always been an area facing many challenges. Phase 1 oncology studies are typically small, open-label, sequential studies enrolling a small sample of adult patients (i.e., 3-6 patients/cohort) in dose escalation. Pediatric evaluations typically lag behind the adult development program. The pediatric starting dose is traditionally referenced on the recommended phase 2 dose in adults with the incorporation of body size scaling. The size of the study is also small and dependent upon the prevalence of the disease in the pediatric population. Similar to adult development, the dose is escalated or de-escalated until reaching the maximum tolerated dose (MTD) that also provides desired biological activities or efficacy. The escalation steps and identification of MTD are often rule-based and do not incorporate all the available information, such as pharmacokinetic (PK), pharmacodynamic (PD), tolerability and efficacy data. Therefore, it is doubtful if the MTD approach is optimal to determine the dosage. Hence, it is important to evaluate whether there is an optimal dosage below the MTD, especially considering the emerging complexity of combination therapies and the long-term tolerability and safety of the treatments. Identification of an optimal dosage is also vital not only for adult patients but for pediatric populations as well. Dosage-finding is much more challenging for pediatric populations due to the limited patient population and differences among the pediatric age range in terms of maturation and ontogeny that could impact PK. Many sponsors defer the pediatric strategy as they are often perplexed by the challenges presented by pediatric oncology drug development (model of action relevancy to pediatric population, budget, timeline and regulatory requirements). This leads to a limited number of approved drugs for pediatric oncology patients. This review article provides the current regulatory landscape, incentives and how they impact pediatric drug discovery and development. We also consider different pediatric cancers and potential clinical trial challenges/opportunities when designing pediatric clinical trials. An outline of how quantitative methods such as pharmacometrics/modelling & simulation can support the dosage-finding and justification is also included. Finally, we provide some reflections that we consider helpful to accelerate pediatric drug discovery and development.

De novo drug design through artificial intelligence: an introduction

Developing new drugs is a complex and formidable challenge, intensified by rapidly evolving global health needs. De novo drug design is a promising strategy to accelerate and refine this process. The recent introduction of Generative Artificial Intelligence (AI) algorithms has brought new attention to the field and catalyzed a paradigm shift, allowing rapid and semi-automatic design and optimization of drug-like molecules. This review explores the impact of de novo drug design, highlighting both traditional methodologies and the recently introduced generative algorithms, as well as the promising development of Active Learning (AL). It places special emphasis on their application in oncological drug development, where the need for novel therapeutic agents is urgent. The potential integration of these AI technologies with established computational and experimental methods heralds a new era in the rapid development of innovative drugs. Despite the promising developments and notable successes, these technologies are not without limitations, which require careful consideration and further advancement. This review, intended for professionals across related disciplines, provides a comprehensive introduction to AI-driven de novo drug design of small organic molecules. It aims to offer a clear understanding of the current state and future prospects of these innovative techniques in drug discovery.

Dose Optimization for Novel Oncology Agents: Design Options and Strategies

Over the past decade, drug development in oncology has shifted from cytotoxic agents to drugs with new mechanisms of action, such as cancer immunotherapies, targeted therapeutics, T-cell engagers and others. The conventional maximum tolerated dose (MTD) based dose-finding paradigm is not suitable for the development of these new agents. Further, health authorities, especially the FDA, are requesting more thorough dose optimization prior to the initiation of pivotal trials, and initiatives such as the FDA’s project Optimus have been launched to accelerate this paradigm shift. Dose optimization is more complicated than finding the MTD and requires consideration of complex mechanisms of action, schedule optimization, long-term drug tolerability, and possibly novel pharmacodynamic endpoints. Thus, thoughtful study designs, translational data, and statistical modeling play an increasingly important role to achieve the goal of dose optimization. This article captures opinions from the 2022 ASA biophamaceutical section regulatory-industry statistics workshop session “Dose finding and optimization for novel oncology agents—the new challenges and novel technologies.” We present general design options for dose optimization. Pros and cons of these design options are discussed, and a real-world case study is provided to illustrate a strategy of dose optimization. Discussions focused on practical considerations are included.

DODII: Bayesian Dose Optimization Design for Randomized Phase II Trials

The traditional MTD-based dose selection paradigm commonly used for cytotoxic chemotherapies might not be optimal for targeted therapies because a higher dose does not necessarily result in improved anti-cancer activity. With the initiation of “Project Optimus” at the FDA, a randomized dose optimization study at the early stage of drug development has become necessary for oncology drug development. We propose a Bayesian Dose Optimization Design for Randomized Phase II trials (DODII) that integrates Bayesian continuous monitoring and Bayesian pick-the-winner approach in a randomized design, where efficacy and toxicity endpoints are jointly used to inform the dose selection. The adaptive feature of the DODII design will terminate a suboptimal dose at an interim analysis under two circumstances: (a) when efficacy or safety data is unpromising, (b) when the other dose option is clearly better. The critical values of the decision rule for the DODII design can be enumerated prior to the initiation of the clinical study, which makes it practically easy to implement. Through simulation studies, we showed that the DODII design has favorable operating characteristics with controlled selection error, higher power to select the optimal dose option, and reduced total sample size as compared to the other commonly used methods.

Framework for the Use of External Controls to Evaluate Treatment Outcomes in Precision Oncology Trials.

Advances in genomics have enabled anticancer therapies to be tailored to target specific genomic alterations. Single-arm trials (SATs), including those incorporated within umbrella, basket, and platform trials, are widely adopted when it is not feasible to conduct randomized controlled trials in rare biomarker-defined subpopulations. External controls (ECs), defined as control arm data derived outside the clinical trial, have gained renewed interest as a strategy to supplement evidence generated from SATs to allow comparative analysis. There are increasing examples demonstrating the application of EC in precision oncology trials. The prospective application of EC in conducting comparative studies is associated with distinct methodological challenges, the specific considerations for EC use in biomarker-defined subpopulations have not been adequately discussed, and a formal framework is yet to be established. In this review, we present a framework for conducting a prospective comparative analysis using EC. Key steps are (1) defining the purpose of using EC to address the study question, (2) determining if the external data are fit for purpose, (3) developing a transparent study protocol and a statistical analysis plan, and (iv) interpreting results and drawing conclusions on the basis of a prespecified hypothesis. We specify the considerations required for the biomarker-defined subpopulations, which include (1) specifying the comparator and biomarker status of the comparator group, (2) defining lines of treatment, (3) assessment of the biomarker testing panels used, and (4) assessment of cohort stratification in tumor-agnostic studies. We further discuss novel clinical trial designs and statistical techniques leveraging EC to propose future directions to advance evidence generation and facilitate drug development in precision oncology.

The Backfill i3+3 Design for Dose-Finding Trials in Oncology

We consider a formal statistical design that allows simultaneous enrollment of a main cohort and a backfill cohort of patients in a dose-finding trial. The goal is to accumulate more information at various doses to facilitate dose optimization. The proposed design, called Bi3+3, combines the simple dose-escalation algorithm in the i3+3 design and a model-based inference under the framework of probability of decisions (POD), both previously published. As a result, Bi3+3 provides a simple algorithm for backfilling patients to lower doses in a dose-finding trial once these doses exhibit safety profile in patients. The POD framework allows dosing decisions to be made when some backfill patients are still being followed with incomplete toxicity outcomes, thereby potentially expediting the clinical trial. At the end of the trial, Bi3+3 uses both toxicity and efficacy outcomes to estimate an optimal biological dose (OBD). The proposed inference is based on a dose-response model that takes into account either a monotone or plateau dose-efficacy relationship, which are frequently encountered in modern oncology drug development. Simulation studies show promising operating characteristics of the Bi3+3 design in comparison to existing designs.

Global Statistics and Data Sciences (GSDS), BeiGene USA, Fulton, MD

Pediatric cancer consists of a diverse group of rare diseases. Due to limited patient populations, standard randomized and controlled trials are often infeasible. As a result, single-arm trials are common in pediatric oncology and the use of external controls is often desirable or necessary to help generate actionable evidence and contextualize trial results. In this paper, we illustrate unique features in pediatric oncology clinical trials and describe their impact on the use of external controls. Various types of relevant external control data sources are described in terms of their utility and drawbacks. Statistical methodologies and design implications with external control are discussed. Two recent case studies using external controls to support pediatric oncology drug development are described in detail.

Global Statistics and Data Sciences (GSDS), BeiGene USA, Fulton, MD

Pediatric cancer consists of a diverse group of rare diseases. Due to limited patient populations, standard randomized and controlled trials are often infeasible. As a result, single-arm trials are common in pediatric oncology and the use of external controls is often desirable or necessary to help generate actionable evidence and contextualize trial results. In this paper, we illustrate unique features in pediatric oncology clinical trials and describe their impact on the use of external controls. Various types of relevant external control data sources are described in terms of their utility and drawbacks. Statistical methodologies and design implications with external control are discussed. Two recent case studies using external controls to support pediatric oncology drug development are described in detail.

Changing trends in phase 1 oncology clinical trials

The Ph1 oncology trial landscape is evolving in response to advances in understanding of cancer biology, novel drug discovery platforms, and therapeutic modalities. To uncover emerging trends in oncology drug development, we identified 7,061 solid tumour Ph1 trials (2009–2021) from clinicaltrials.gov to determine the numbers of trials commenced, therapeutic classes, combinations, tumour streams, and geographical distribution. Ph1 oncology trials increased by an average of 5.2 %/year. There was a significant relative increase in the number of immunotherapy studies and a significant relative decrease in trials containing chemotherapy. Between 2009 and 2021, multi-agent combination trials outnumbered single-agent trials and single-class trials outnumbered multimodal combination trials. The proportion conducted in the Asia-Pacific significantly increased. Multiregional trials decreased during the COVID-19 pandemic, reducing projected trial numbers in Asia-Pacific and Europe whilst increasing single-region trials in North America. Further study is required to track recovery post-pandemic, and the emergence of novel modalities (e.g. ADCs and cellular therapies).

Subgroup identification of targeted therapy effects on biomarker for time to event data.

The initiation biomarker-driven trials have revolutionized oncology drug development by challenging the traditional phased approach and introducing basket studies. Notable successes in non-small cell lung cancer (NSCLC) with ALK, ALK/ROS1, and EGFR inhibitors have prompted the need to expand this approach to other cancer sites. This study explores the use of dose response modeling and time-to-event algorithms on the biomarker molecular targeted agent (MTA). By simulating subgroup identification in MTA-related time-to-event data, the study aims to develop statistical methodology supporting biomarker-driven trials in oncology. A total of n patients are selected assigned for different doses. A dataset is prepared to mimic the situation on Subgroup Identification of MTA for time to event data analysis. The response is measured through MTA. The MTA value is also measured through ROC. The Markov Chain Monte Carlo (MCMC) techniques are prepared to perform the proposed algorithm. The analysis is carried out with a simulation study. The subset selection is performed through the Threshold Limit Value (TLV) by the Bayesian approach. The MTA is observed with range 12-16. It is expected that there is a marginal level shift of the MTA from pre to post-treatment. The Cox time-varying model can be adopted further as causal-effect relation to establishing the MTA on prolonging the survival duration. The proposed work in the statistical methodology to support the biomarker-driven trial for oncology research. This study extends the application of biomarker-driven trials beyond NSCLC, opening possibilities for implementation in other cancer sites. By demonstrating the feasibility and efficacy of utilizing MTA as a biomarker, the research lays the foundation for refining and validating biomarker use in clinical trials. These advancements aim to enhance the precision and effectiveness of cancer treatments, ultimately benefiting patients.

An Analysis of Dosing-Related Postmarketing Requirements for Novel Oncology Drugs Approved by the U.S. Food and Drug Administration, 2012-2022.

The FDA's Oncology Center of Excellence's (OCE) launch of Project Optimus signals increased focus on dose optimization approaches in oncology drug development, particularly toward optimization in the premarket setting. Although sponsors continue to adapt premarket study designs and approaches to align with FDA's expectations for dose optimization, including consideration of the optimal dosage(s), there are still instances where questions remain at the time of approval about whether the approved doses or schedules are optimal. In these cases, FDA can exercise regulatory flexibility by issuing postmarketing requirements (PMR) and avoid delaying patient access to promising therapies. This landscape analysis demonstrates that over the past decade (2012-2022), FDA frequently used PMRs to answer additional questions about dosing for novel oncology approvals. We found more than half of drugs (78/132, 59.1%) had a dosing PMR and observed a recent increase in PMRs intended to evaluate whether a lower dose could be more optimal. These results suggest there are opportunities to adapt premarket dose optimization strategies and leverage innovative development tools to ensure timely identification of the optimal dose.

Abstract A007: Preclinical bone metastasis technology platform – Predictive evaluation of experimental therapies on bone metastasis

Abstract Most cancer deaths are due to metastases, and bone metastases are a considerable problem especially in breast and prostate cancer, being developed in 70-90% of advanced-stage patients. Despite major investments in oncology drug development, bone metastases are currently incurable and a high unmet medical need with only 5% of patients being alive 5 years after the diagnosis. Development of therapies for bone metastasis has been challenging due to lack of appropriate preclinical models available to support decision making in next phases of drug development. In the absence of clinically relevant preclinical bone metastasis models, the current strategy is to rely on preclinical efficacy data obtained with subcutaneous models that lack the clinically relevant local tissue microenvironment, which has a major impact on tumor growth. The use of clinically non-relevant models in preclinical-stage development may be one important reason for the current >95% failure rate of oncology drugs in clinical trials. To support predictive evaluation of therapies for bone metastatic cancers, we describe a preclinical bone metastasis technology platform for evaluating efficacy of novel therapies on bone metastases. The platform utilizes tumors growing in bone microenvironment, mimicking growth of bone metastases in patients. Syngeneic or humanized mouse models with tumor and immune cells of same species are needed for supporting development of immunotherapies, allowing interactions of tumor and immune cells in the bone metastatic microenvironment, according to the novel osteoimmuno-oncology concept. The platform provides a predictive tool for studying unique biological features associated with different types of bone metastases in cancer-type specific manner. Here we summarize case examples where results from preclinical bone metastasis studies align with clinical findings of different therapies approved or evaluated for bone metastasis. Zoledronic acid, an anti-resorptive bisphosphonate that is currently used in breast cancer patients with bone metastases to prevent cancer-induced bone loss, showed improved bone health but no effects on tumor growth. Radium-223 dichloride, an approved treatment for bone metastatic castration-resistant prostate cancer in patients, showed reduced prostate cancer growth and decreased tumor-induced bone changes. As for immunotherapies, an IDO1 inhibitor had no effects on breast cancer bone metastases and the anti-PD-1 antibody pembrolizumab had no effects on breast and prostate cancer bone metastases, predicting recent clinical findings that demonstrate lack of efficacy of anti-PD-1 in clinical prostate cancer trials. We conclude that the bone metastasis technology platform is a biologically relevant tool for preclinical evaluation of the efficacy of experimental therapies on bone metastasis, and it has been validated with positive and negative case examples, demonstrating its clinically predictive power. Citation Format: Tiina E Kähkönen, Jussi M Halleen, Jenni Bernoulli. Preclinical bone metastasis technology platform – Predictive evaluation of experimental therapies on bone metastasis [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A007.