Successful Drug Development Research Articles

Clinical research vehicles as a modality for medical research education and conduct of decentralized trials, supporting justice, equity, and diversity in research.

Current clinical research lacks diversity in those that participate. This lack of diversity is concerning given its importance for successful drug development. The frequency and severity of many diseases, along with the pharmacological properties of therapies, can display significant differences based on patient diversity. A clinical trial population that is more reflective of these differences will help researchers better understand the therapeutic profile of the treatment and provide generalizable knowledge to the medical community. The advent of decentralized clinical trial designs is meant to help address this lack of diversity by using portable digital health technologies and virtual interactions to enhance clinical trial access and broaden participation. By leveraging these technologies, trial conduct can occur at locations other than traditional research sites. This shift in trial location may help address some of the logistical, educational, engagement, and trust barriers that have historically prevented enrollment of diverse populations. However, these types of trials still have limitations. Ethical concerns around justice, equity, and diversity will still exist with decentralized clinical trials, which could be mediated using clinical research vehicles. When utilized, this modality may enhance the scientific design and conduct of clinical trials and better follow these ethical principles. These enhancements and improved ethical direction could be accomplished through increasing community involvement, improving health literacy, supporting more diverse trial sites, creating community-based research footholds, fostering connections with researchers, limiting technical challenges, and preventing data security issues.

Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?

Despite implementing hundreds of strategies, cancer drug development suffers from a 95% failure rate over 30 years, with only 30% of approved cancer drugs extending patient survival beyond 2.5 months. Adding more criteria without eliminating nonessential ones is impractical and may fall into the "survivorship bias" trap. Machine learning (ML) models may enhance efficiency by saving time and cost. Yet, they may not improve success rate without identifying the root causes of failure. We propose a "STAR-guided ML system" (structure-tissue/cell selectivity-activity relationship) to enhance success rate and efficiency by addressing three overlooked interdependent factors: potency/specificity to the on/off-targets determining efficacy in tumors at clinical doses, on/off-target-driven tissue/cell selectivity influencing adverse effects in the normal organs at clinical doses, and optimal clinical doses balancing efficacy/safety as determined by potency/specificity and tissue/cell selectivity. STAR-guided ML models can directly predict clinical dose/efficacy/safety from five features to design/select the best drugs, enhancing success and efficiency of cancer drug development.

Approved medicines for paediatric solid tumours in Europe: Lessons from the life cycle of a paediatric investigation plan

BackgroundDespite the positive changes brought by the Paediatric Regulation in the European Union (EU) in 2007, drug development in children remains challenging. MethodsTo better understand the issues encountered to reach an authorisation for paediatric patients, we reviewed the pathway of the 11 Paediatric Investigational Plans (PIPs) with indications targeting paediatric solid tumours granted by the Committee for Medicinal Products for Human Use (CHMP) between 2007 and 2022. ResultsOn average 5,5 years were necessary to reach approval after a PIP was agreed. All the PIPs underwent at least one modification (median 3 modifications per PIP). The use of single arm trials, in the context of refractory/relapsed disease in absence of standard of care treatment, was supportive for granting a paediatric indication in the majority of the cases. In 6 out of 11 approved products, extrapolation from adults was used. For 2/11 the approval focused on an older population first compared to the initial age group agreed in the PIP due to the development of a suitable formulation for younger children still ongoing at the time of first approval. Scientific advice sought on paediatric development use of extrapolation from adults, major objections raised by CHMP and post-marketing requirements were examined. ConclusionAnalysing the process necessary to reach an authorisation for paediatric patients, we highlight the major challenges faced in the paediatric approval process and the positive examples of successful drug development that reached final approval. Our analysis is expected to provide useful insights to drug developers, investigators and regulators.

A best practices framework for spatial biology studies in drug discovery and development: enabling successful cohort studies using digital spatial profiling

ABSTRACT The discovery of biomarkers, essential for successful drug development, is often hindered by the limited availability of tissue samples, typically obtained through core needle biopsies. Standard ‘omics platforms can consume significant amounts of tissue, forcing scientist to trade off spatial context for high-plex assays, such as genome-wide assays. While bulk gene expression approaches and standard single-cell transcriptomics have been valuable in defining various molecular and cellular mechanisms, they do not retain spatial context. As such, they have limited power in resolving tissue heterogeneity and cell–cell interactions. Current spatial transcriptomics platforms offer limited transcriptome coverage and have low throughput, restricting the number of samples that can be analyzed daily or even weekly. While the Digital Spatial Profiling (DSP) method does not provide single-cell resolution, it presents a significant advancement by enabling scalable whole transcriptome and ultrahigh-plex protein analysis from distinct tissue compartments and structures using a single tissue slide. These capabilities overcome significant constraints in biomarker analysis in solid tissue specimens. These advancements in tissue profiling play a crucial role in deepening our understanding of disease biology and in identifying potential therapeutic targets and biomarkers. To enhance the use of spatial biology tools in drug discovery and development, the DSP Scientific Consortium has created best practices guidelines. These guidelines, built on digital spatial profiling data and expertise, offer a practical framework for designing spatial studies and using current and future spatial biology platforms. The aim is to improve tissue analysis in all research areas supporting drug discovery and development.

The Hard Way? Learning and Drug Development Success (or Failure) in the Pharmaceutical Industry

The Hard Way? Learning and Drug Development Success (or Failure) in the Pharmaceutical Industry

Pharmacological induction of the hypoxia response pathway in Huh7 hepatoma cells limits proliferation but increases resilience under metabolic stress

The hypoxia response pathway enables adaptation to oxygen deprivation. It is mediated by hypoxia-inducible factors (HIF), which promote metabolic reprogramming, erythropoiesis, angiogenesis and tissue remodeling. This led to the successful development of HIF-inducing drugs for treating anemia and some of these molecules are now in clinic. However, elevated levels of HIFs are frequently associated with tumor growth, poor prognosis, and drug resistance in various cancers, including hepatocellular carcinoma (HCC). Consequently, there are concerns regarding the recommendation of HIF-inducing drugs in certain clinical situations. Here, we analyzed the effects of two HIF-inducing drugs, Molidustat and Roxadustat, in the well-characterized HCC cell line Huh7. These drugs increased HIF-1α and HIF-2α protein levels which both participate in inducing hypoxia response genes such as BNIP3, SERPINE1, LDHA or EPO. Combined transcriptomics, proteomics and metabolomics showed that Molidustat increased the expression of glycolytic enzymes, while the mitochondrial network was fragmented and cellular respiration decreased. This metabolic remodeling was associated with a reduced proliferation and a lower demand for pyrimidine supply, but an increased ability of cells to convert pyruvate to lactate. This was accompanied by a higher resistance to the inhibition of mitochondrial respiration by antimycin A, a phenotype confirmed in Roxadustat-treated Huh7 cells and Molidustat-treated hepatoblastoma cells (Huh6 and HepG2). Overall, this study shows that HIF-inducing drugs increase the metabolic resilience of liver cancer cells to metabolic stressors, arguing for careful monitoring of patients treated with HIF-inducing drugs, especially when they are at risk of liver cancer.

Natural Products Have Increased Rates of Clinical Trial Success throughout the Drug Development Process.

Natural products (NPs) or their derivatives represent a large proportion of drugs that successfully progress through clinical trials to approval. This study explores the presence of NPs in both early- and late-stage drug discovery to determine their success rate, and the factors or features of natural products that contribute to such success. As a proxy for early drug development stages, we analyzed patent applications over several decades, finding a consistent proportion of NP, NP-derived, and synthetic-compound-based patent documents, with the latter group outnumbering NP and NP-derived ones (approximately 77% vs 23%). We next assessed clinical trial data, where we observed a steady increase in NP and NP-derived compounds from clinical trial phases I to III (from approximately 35% in phase I to 45% in phase III), with an inverse trend observed in synthetics (from approximately 65% in phase I to 55% in phase III). Finally, in vitro and in silico toxicity studies revealed that NPs and their derivatives were less toxic alternatives to their synthetic counterparts. These discoveries offer valuable insights for successful NP-based drug development, highlighting the potential benefits of prioritizing NPs and their derivatives as starting points.

Dynamics Playing a Key Role in the Covalent Binding of Inhibitors to Focal Adhesion Kinase.

Covalent kinase inhibitors (CKIs) have recently garnered considerable attention, yet the rational design of CKIs continues to pose a great challenge. In the discovery of CKIs targeting focal adhesion kinase (FAK), it has been observed that the chemical structure of the linkers plays a key role in achieving covalent targeting of FAK. However, the mechanism behind the observation remains elusive. In this work, we employ a comprehensive suite of advanced computational methods to investigate the mechanism of CKIs covalently targeting FAK. We reveal that the linker of an inhibitor influences the contacts between the warhead and residue(s) and the residence time in active conformation, thereby dictating the inhibitor's capability to bind covalently to FAK. This study reflects the complexity of CKI design and underscores the importance of considering the dynamic interactions and residence times for the successful development of covalent drugs.

Leveraging pharmacokinetic parameters as covariate in Bayesian logistic regression model to optimize dose selection in early phase oncology trial

ABSTRACT Dose selection and optimization in early phase of oncology drug development serves as the foundation for the success of late phases drug development. Bivariate Bayesian logistic regression model (BLRM) is a widely utilized model-based algorithm that has been shown to improve the accuracy for identifying recommended phase 2 dose (RP2D) based on dose-limiting-toxicity (DLT) over traditional method such as 3 + 3. However, it remains a challenge to optimize dose selection that strikes a proper balance between safety and efficacy in escalation and expansion phase of phase I trials. In this paper, we first use a phase I clinical trial to demonstrate how the variability of drug exposure related to pharmacokinetic (PK) parameters among trial participants may add to the difficulties of identifying optimal dose. We use simulation to show that concurrently or retrospectively fitting BLRM model for dose/toxicity data from escalation phase with dose-independent PK parameters as covariate lead to improved accuracy of identifying dose level at which DLT rate is within a prespecified toxicity interval. Furthermore, we proposed both model- and rule-based methods to modify dose at patient level in expansion cohorts based on their PK/exposure parameters. Simulation studies show this approach leads to higher likelihood for a dose level with a manageable toxicity and desirable efficacy margin to be advanced to late phase pipeline after being screened at expansion phase of phase I trial.

Unveiling new genetic insights in rheumatoid arthritis for drug discovery through Taxonomy3 analysis

Genetic support for a drug target has been shown to increase the probability of success in drug development, with the potential to reduce attrition in the pharmaceutical industry alongside discovering novel therapeutic targets. It is therefore important to maximise the detection of genetic associations that affect disease susceptibility. Conventional statistical methods such as genome-wide association studies (GWAS) only identify some of the genetic contribution to disease, so novel analytical approaches are required to extract additional insights. C4X Discovery has developed Taxonomy3, a unique method for analysing genetic datasets based on mathematics that is novel in drug discovery. When applied to a previously published rheumatoid arthritis GWAS dataset, Taxonomy3 identified many additional novel genetic signals associated with this autoimmune disease. Follow-up studies using tool compounds support the utility of the method in identifying novel biology and tractable drug targets with genetic support for further investigation.

The emerging role of targeted protein degradation to treat and study cancer.

The evolution of cancer treatment has provided increasingly targeted strategies both in the upfront and relapsed disease settings. Small-molecule inhibitors and immunotherapy have risen to prominence with chimeric antigen receptor T-cells, checkpoint inhibitors, kinase inhibitors, and monoclonal antibody therapies being deployed across a range of solid organ and haematological malignancies. However, novel approaches are required to target transcription factors and oncogenic fusion proteins that are central to cancer biology and have generally eluded successful drug development. Thalidomide analogues causing protein degradation have been a cornerstone of treatment in multiple myeloma, but a lack of in-depth mechanistic understanding initially limited progress in the field. When the protein cereblon (CRBN) was found to mediate thalidomide analogues' action and CRBN's neo-targets were identified, existing and novel drug development accelerated, with applications outside multiple myeloma, including non-Hodgkin's lymphoma, myelodysplastic syndrome, and acute leukaemias. Critically, transcription factors were the first canonical targets described. In addition to broadening the application of protein-degrading drugs, resistance mechanisms are being overcome and targeted protein degradation is widening the scope of druggable proteins against which existing approaches have been ineffective. Examples of targeted protein degraders include molecular glues and proteolysis targeting chimeras (PROTACs): heterobifunctional molecules that bind to proteins of interest and cause proximity-induced ubiquitination and proteasomal degradation via a linked E3 ligase. Twenty years since their inception, PROTACs have begun progressing through clinical trials, with early success in targeting the oestrogen receptor and androgen receptor in breast and prostate cancer respectively. This review explores important developments in targeted protein degradation to both treat and study cancer. It also considers the potential advantages and challenges in the translational aspects of developing new treatments. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Risky Business: Human Genetics Improves Drug Development Success

Risky Business: Human Genetics Improves Drug Development Success

BiSpec Pairwise AI: guiding the selection of bispecific antibody target combinations with pairwise learning and GPT augmentation

PurposeBispecific antibodies (BsAbs), capable of targeting two antigens simultaneously, represent a significant advancement by employing dual mechanisms of action for tumor suppression. However, how to pair targets to develop effective and safe bispecific drugs is a major challenge for pharmaceutical companies.MethodsUsing machine learning models, we refined the biological characteristics of currently approved or in clinical development BsAbs and analyzed hundreds of membrane proteins as bispecific targets to predict the likelihood of successful drug development for various target combinations. Moreover, to enhance the interpretability of prediction results in bispecific target combination, we combined machine learning models with Large Language Models (LLMs). Through a Retrieval-Augmented Generation (RAG) approach, we supplement each pair of bispecific targets’ machine learning prediction with important features and rationales, generating interpretable analytical reports.ResultsIn this study, the XGBoost model with pairwise learning was employed to predict the druggability of BsAbs. By analyzing extensive data on BsAbs and designing features from perspectives such as target activity, safety, cell type specificity, pathway mechanism, and gene embedding representation, our model is able to predict target combinations of BsAbs with high market potential. Specifically, we integrated XGBoost with the GPT model to discuss the efficacy of each bispecific target pair, thereby aiding the decision-making for drug developers.ConclusionThe novelty of this study lies in the integration of machine learning and GPT techniques to provide a novel framework for the design of BsAbs drugs. This holistic approach not only improves prediction accuracy, but also enhances the interpretability and innovativeness of drug design.

What facets of physical function are most important to adults diagnosed with cancer?

Successful patient-focused drug development involves selecting and measuring outcomes in clinical trials that are important to patients. The U.S. Food & Drug Administration's definition of clinical benefit includes how patients feel, function, or survive. Patients are considered the experts in describing how they feel and function. In cancer trials, patient-reported measures of physical function provide insight into how patients function at baseline, benefit from the interventions being studied, and the impact of treatment side effects. We conducted a qualitative study with adults diagnosed with cancer to describe facets of physical function from their perspective and to identify which facets are most important to this patient population. Using concept elicitation and cognitive interviewing techniques, we conducted semi-structured interviews with 72 adults ≥ 22years of age with cancer who received treatment with an anticancer drug or biologic within six months of the interview. We selected participants using purposive sampling with the aim to elicit diverse experiences regarding how they may interpret and respond to questions related to physical function. Participants were presented with patient-reported outcome (PRO) items representative of PRO measures used in cancer and general populations. Five facets of how physical function relates to activities were defined from the patient perspective: ability, difficulty, limitation, satisfaction, and completion. More than half of the participants indicated that ability was the most important facet of physical function. The next most important were satisfaction (18.3%), limitation (14.1%), difficulty (5.6%), and completion (2.8%). This study demonstrates that we must be more specific about the facets of physical function that we set out to assess when we use PRO measures to describe the patient experience. These results have implications for the specificity of physical function facets when measured in cancer clinical trials.

Analysis and Reflection on Investment Status of Innovative Drugs in China

At present, China's innovative drug support policies and market volume are good, but China's innovative drug research and development(R&D) success rate and market share still need to be improved. This is related to the unreasonable investment direction of innovative drugs and the low utilization efficiency of R&D resources. Therefore, the objective of this paper is to evaluate China's present state of investment in innovation drugs and explore how to promote the effectiveness and sustainability of innovative drug investment. Through data analysis, this paper found that the scale and frequency of investment and financing of innovative drugs in China are declining. The problem with China's innovative drug investment is that the valuation of hot projects is too high, the allocation of funds is unreasonable, the investment is not in line with the actual needs of the industry, and only the pursuit of short-term returns. Given the various problems in China's innovative drug investment, the government needs to create a long-term stable environment for the innovative drug investment market and prudently formulate relevant policies to strike a balance between stimulating the market and regulating the market. Entrepreneurs should choose the direction of R&D according to their areas of expertise combined with cutting-edge scientific research progress rather than adjusting the direction of R&D to cater to the preferences of investment institutions. Investment institutions should avoid risks caused by unreasonable capital allocation by strengthening the layout of the pre-investment track and prudently carrying out project valuation analysis.

Abstract 562: Frequent copy number gain of MCL1 is a therapeutic target for osteosarcoma

Abstract Osteosarcoma (OS) is the most common primary malignant bone tumor that predominantly occurs in children, adolescents, and young adults. The treatment for OS that combines surgery with chemotherapy, which consists of a four-drug combination of adriamycin (DOX), cisplatin (CDDP), high-dose methotrexate (MTX), and ifosfamide, was established in 1970s, and it is still used as a standard therapy. Oncogenic driver mutations and fusion genes common to OS have not been identified, which is one of the reasons for the lack of success in drug development for OS. Herein, to explore the characteristic vulnerability in OS, molecular targeted drugs were screened against OS cell lines and patient-derived cells; obatoclax, a pan-BH3 mimetic, and a concomitant drug that enhances obatoclax-induced apoptosis, OSI906, a dual inhibitor of IGF1R and IR, were found. Fluorescence in situ hybridization (FISH) analysis revealed that copy number gains of MCL1, an antiapoptotic Bcl-2 family protein, were observed in 46.3% of formalin-fixed paraffin-embedded (FFPE) specimens from patients with OS (N = 41) and in 45.5% of OS cell lines (N = 11), thereby causing vulnerability to BH3 mimetics. IHC staining also revealed high expression of Mcl-1 protein in specimens with MCL1-amplification in FISH analysis. Pharmacological or genetic inhibition of Mcl-1 induced potent apoptosis in MCL1-amplified OS cells, which was enhanced by the co-inhibition with Bcl-xL. Moreover, chromosome 1q21.2-3, where MCL1 is located, contains several IGF1R/PI3K pathway-related genes, including PIP5K1A, TARS2, OUTD7B, and ENSA, whose copy numbers were also increased in MCL1-amplified OS cells, suggesting that 1q21.2-3-amplified OS cells readily activate downstream pathways via IGF1R. IGF signaling activation in 1q21.2-3-amplified OS cells negated the obatoclax-induced apoptosis, which was overcome by OSI906 treatment. Combined obatoclax and OSI906 administration remarkably suppressed MCL1-amplified OS tumor growth in the xenograft mouse model and resulted in tumor regression; however, none of the treatments reduced tumor growth of OS without MCL1 amplification. These results suggest that combining obatoclax with OSI906 could be a potential therapeutic intervention for 1q21.2-3-amplified OS, and that prior stratification of OS based on the 1q21.2-3 amplification status would be important for the success of this combination therapy. Thus, we elucidated the mechanism of action of drugs and their potential as a novel therapeutic strategy for approximately 50% of patients with OS. Moreover, a FISH analysis that can detect copy number gains of MCL1 in FFPE specimens from patients with OS was established to stratify patients eligible for this therapy. Citation Format: Satoshi Takagi, Ryohei Katayama. Frequent copy number gain of MCL1 is a therapeutic target for osteosarcoma [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 562.

Abstract 7497: Advancing multi-biomarker CTC assay and CDx development through the automated GenoCTC system

Abstract Introduction: The successful outcomes observed in clinical trials emphasize the effectiveness of therapeutic approaches relying on biomarker tests, particularly in the development of cancer drugs. The parallel development of a drug and its associated predictive biomarker is considered an ideal strategy for achieving rapid and successful drug development in precision medicine. In this context, we employ an automated circulating tumor cell (CTC) isolation platform, known as GenoCTC, to facilitate the development of biomarker-driven CTC assays, with the ultimate goal of developing companion diagnostic (CDx) tests. Method: This study employed the 5th generation GenoCTC device, a fully automated system designed for marker-based CTC enrichment using immune magnetophoresis. We confirmed the isolation of CTCs through spiking tests, utilizing cancer biomarkers such as cMET, Mesothelin (MSLN), and Claudin 3 (CLDN3). cMET is a biomarker extensively studied in various cancers, particularly non-small cell lung cancer. We previously showed the prognostic significance of cMET expressing CTCs in HR+ and HER2- metastatic breast cancer patients. MSLN and CLDN3 are tumor-associated proteins showing overexpression across diverse cancer types, with a potential for the development of antibody-based cytotoxic drugs. The GenoCTC device allows efficient separation of CTCs expressing these biomarkers, ensuring high accuracy, precision, specificity, and sensitivity. Simultaneously, therapeutic drugs directed at these markers are progressing through phase trials or preclinical studies. Conclusion: Our results show that cMET, MSLN, and CLDN3 are potential markers for the development of biomarker tests, that can improve the patient selection process for personalized therapy targeting these biomarkers. Citation Format: Chaithanya Chelakkot, Jieun Park, Chae Rin Kim, Yeonu Lee, Yu Rim Lee, Saehyung Lee, Jun Young Choi, Young Ho Moon, Yoon-La Choi, Young Kee Shin, Hun Seok Lee. Advancing multi-biomarker CTC assay and CDx development through the automated GenoCTC system [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 7497.

Abstract 6768: Matched XPDX™derivative models as a platform for continuity across preclinical drug development

Abstract Drug discovery is a reductive process in which druglike entities are carried through increasingly complex assay formats to tease out the compounds best suited for human studies. In vivo efficacy remains the benchmark for successful preclinical drug development. Cell-based models are a mainstay in early stages but translatability of different models across platforms remains a challenge. In oncology, patient-derived xenografts (PDX) are the gold standard for animal studies because they recapitulate key features of human tumors including the cellular heterogeneity, growth patterns, and drug response of the original patient tumor. Ex vivo, PDX derivatives have the potential to harness these same traits thereby extending the utility of PDX models to earlier stages of preclinical drug development. The KIYA-Predict™ platform is a clinically validated tool for the assessment of ex vivo drug responses across a broad range of drug classes that when applied to XPDX models shows good correlation to in vivo drug responses and excellent intra-sample reproducibility over time. Working with the well-characterized XenoSTART™ XPDX model repository, Kiyatec has derived 2D cell lines and organotypic 3D-expansion cultures to complement the ex vivo predictive value of our KIYA-PREDICT™ XPDX drug response profiling (DRP) platform. The development of syngeneic models applicable to simpler, high-throughput library screening (2D formats) and lower-throughput more complex mechanistic studies (3D organotypic models) could alleviate the burden and pitfalls of extrapolating data across unrelated models. Matched sets of 2D cell lines (2D-XPDX™) and 3D organotypic (XPDXO™) cultures were established from single cell suspensions of digested, mouse-cell depleted XPDX™ tissue samples. Drug response profiling of the XPDX™ in their various ex vivo culture formats showed relative similarities across platforms. Genomic profiling of the derivates confirmed shared tissue origin and retention of genomic and phenotypic signatures to varying degrees across models. Mechanistic evaluation of 3D organotypic culture demonstrated that disease-relevant biomarkers and processes are maintained. Together these results demonstrate the feasibility of this novel drug discovery platform that avoids ambiguity imparted by discordant models. Citation Format: Melissa Millard, Aaron Carlson, Danielle Y. Nadeau, Kimberly J. Burgess, Alyssa Simonson, Armando Diaz, Michael Wick, Teresa DesRochers. Matched XPDX™derivative models as a platform for continuity across preclinical drug development [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 6768.

Abstract 2389: Empowering early drug development in neoadjuvant non-small cell lung cancer studies using an innovative proof-of-concept platform trial design

Abstract Several platform trial designs have recently been proposed in oncology. However, developing an efficient one in the neoadjuvant non-small cell lung cancer (NSCLC) is not easy due to the difficulties in patient recruitment and limited relevant data. These obstacles hinder the inclusion of a control arm, thereby losing the ability to make an informed Go/No-Go decision, and to assess prognostic and predictive biomarkers. To overcome these challenges, we have developed an innovative and efficient proof-of-concept platform trial design that aims to empower drug development in early stage of neoadjuvant NSCLC. The proposed platform trial incorporates available experimental agents, allowing for new agents to be added when available. Within the master protocol structure, a small control arm is included, only about 20% of experimental arm size, and is shared by all experimental arms for the analysis purpose. Consequently, the resulting direct comparison with the control arm enables an informed Go/No-Go decision-making, while the total sample size and budget impacts are minimized. The flexibility of Go/No-Go decision-making is further enhanced in a 3-outcome decision-making framework where a consider zone is introduced for physicians to examine the totality of data. Two innovative statistical techniques are incorporated: Bayesian dynamic borrowing for current control, and response-adaptive randomization (RAR). The former helps reduce the sample size, and the latter aims to optimize patient allocation and improve the ability to identify promising agents. Simulation results demonstrate that the direct comparison sharply reduces the false positive rate below 10%, compared to a single-arm design setting. The effective sample size of the control arm is statistically augmented through Bayesian dynamic borrowing. The patient allocation is guided based on the agent’s efficacy data with a well-calibrated RAR. The promising agent receives meaningfully more patients than if a fixed-randomization is used, while the variability in patient allocation is decreased. Compared to a traditional design, our innovative design boosts the probability of identifying promising agent by up to 2-fold. To conclude, this research develops an innovative platform trial design to improve the success of drug development in early stage NSCLC. The master protocol structure, inclusion of a small control arm, and dynamic borrowing, increase the plausibility of conducting a trial in the neoadjuvant population. Advanced statistical features optimize the patient allocation and enhance the ability to identify promising drugs. Lastly, the proposed design can be easily applied to other oncology indications with appropriate modifications. Citation Format: Zhe Chen, Phillip A. Dennis, Ji Lin. Empowering early drug development in neoadjuvant non-small cell lung cancer studies using an innovative proof-of-concept platform trial design [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 2389.

Antibody druggability screening process and evaluation strategy

Since the approval of OKT3 as the first therapeutic monoclonal antibody in 1986, there has been rapid development in antibody technology and antibody drugs. Monoclonal antibodies, antibody fragments, bi (multi) specific antibodies, fusion proteins, nanobodies, and antibody-drug conjugates (ADCs) have been introduced and play a significant role in the treatment of oncology, hematology, immunology, respiratory, metabolic and other related diseases. The process of antibody drug discovery involves multiple rounds of biological function and druggability assessments to identify the best candidate sequences that are safe, effective, stable, and scalable. This lays the foundation for the efficiency and success of drug development and clinical studies. In the phase of antibody drug discovery, "druggability screening and evaluation" has received increasing attention. It involves drug discovery and design, screening and optimization of lead molecules as well as the validation of candidate molecules, with the aim of detecting potential physicochemical risk factors and evaluating controllability to ensure the quality stability of the subsequent drug development process. This paper classifies and defines the process of druggability screening and evaluation in the antibody discovery phase, covering monoclonal antibodies, bispecific antibodies, nanobodies, ADCs and other related technologies and drug forms. It also summarizes the quality attributes and high-throughput detection technology that should be emphasized in the druggability screening and evaluation. The systematic elaboration of the druggability development process and strategy provides a reference for the druggability screening and evaluation of emerging innovative drugs, significantly improving the efficiency and success rate of antibody drug development.