Early Phases Of Drug Development Research Articles

A suction blister model to characterize epidermal wound healing and evaluate the efficacy of the topical wound healing agent INM-755 in healthy volunteers.

Non-healing wounds represent a substantial medical burden with few effective treatments available. To address this challenge, we developed a novel epidermal wound healing model using suction blisters in healthy volunteers. This model allowed for the comprehensive assessment of wound healing dynamics and the evaluation of INM-755, a topical cream containing cannabinol, as a potential therapeutic agent. Two clinical studies were conducted: an observational study and an interventional study. In both studies, healthy volunteers underwent a suction blister procedure on their lower back, creating open epidermal wounds. Wound healing parameters were assessed using advanced imaging systems. Skin barrier function and perfusion were evaluated through trans epidermal water loss (TEWL) and dynamic optical coherence tomography (D-OCT), respectively. The observational study demonstrated the successful and reproducible induction of blisters and the removal of epidermal sheet, enabling quantifiable measurements of wound healing parameters over time. Re-epithelialization was observed, revealing recovery of skin barrier function and perfusion. In the interventional study, differences of treatments over time were quantified using the above-described techniques. Despite differences from disease-specific blistering, our developed model provides a valuable platform for studying wound healing mechanisms and assessing novel therapeutic interventions. The sensitivity to treatment effects demonstrated in our study underscores the potential utility of this model in early-phase clinical drug development programs targeting wound healing disorders.

T3 + 3: 3 + 3 Design With Delayed Outcomes.

Delayed outcome is common in phase I oncology clinical trials. It causes logistic difficulty, wastes resources, and prolongs the trial duration. This article investigates this issue and proposes the time-to-event 3 + 3 (T3 + 3) design, which utilizes the actual follow-up time for at-risk patients with pending toxicity outcomes. The T3 + 3 design allows continuous accrual without unnecessary trial suspension and is costless and implementable with pretabulated dose decision rules. Besides, the T3 + 3 design uses the isotonic regression to estimate the toxicity rates across dose levels and therefore can accommodate for any targeted toxicity rate for maximum tolerated dose (MTD). It dramatically facilitates the trial preparation and conduct without intensive computation and statistical consultation. The extension to other algorithm-based phase I dose-finding designs (e.g., i3 + 3 design) is also studied. Comprehensive computer simulation studies are conducted to investigate the performance of the T3 + 3 design under various dose-toxicity scenarios. The results confirm that the T3 + 3 design substantially shortens the trial duration compared with the conventional 3 + 3 design and yields much higher accuracy in MTD identification than the rolling six design. In summary, the T3 + 3 design addresses the delayed outcome issue while keeping the desirable features of the 3 + 3 design, such as simplicity, transparency, and costless implementation. It has great potential to accelerate early-phase drug development.

New limits proposed for the management of non-mutagenic impurities

Multiple international guidelines exist that describe both quality and safety considerations for the control of the broad spectrum of impurities inherent to drug substance and product manufacturing processes. However, regarding non-mutagenic impurities (NMI) the most relevant ICH Q3A/B guidelines are not applicable during early phases of drug development leading to confusion about acceptable limits at this stage. Thus, there is need for more flexible approaches that ensure that patient safety remains paramount, while taking into consideration the limited duration of exposure. An EFPIA survey, which collected quantitative data from different types of studies applied to qualify impurities in accordance with ICH Q3A, shows that no toxicities could be attributed to any of the 467 impurities at any tested level in vivo. This data combined with earlier published toxicological datasets encompassing drug substances and intermediates, food related substances and chemicals provide convincing evidence that for NMIs, the application of a generic 5 mg/day limit for an exposure duration <6 months, and a 1 mg/day generic limit for life-long exposure, provides sufficient margins to ensure patient safety. Hence, application of these absolute limits to trigger qualification studies (instead of the relative limits described in Q3A/B), is considered warranted. This approach will prevent conduct of unnecessary dedicated impurity qualification studies and the resulting use of animals.

The Conventional and Breakthrough Tool for the Study of L-Glutamate Transporters.

In our recent report, we clarified the direct interaction between the excitatory amino acid transporter (EAAT) 1/2 and polyunsaturated fatty acids (PUFAs) by applying electrophysiological and molecular biological techniques to Xenopus oocytes. Xenopus oocytes have a long history of use in the scientific field, but they are still attractive experimental systems for neuropharmacological studies. We will therefore summarize the pharmacological significance, advantages (especially in the study of EAAT2), and experimental techniques that can be applied to Xenopus oocytes; our new findings concerning L-glutamate (L-Glu) transporters and PUFAs; and the significant outcomes of our data. The data obtained from electrophysiological and molecular biological studies of Xenopus oocytes have provided us with further important questions, such as whether or not some PUFAs can modulate EAATs as allosteric modulators and to what extent docosahexaenoic acid (DHA) affects neurotransmission and thereby affects brain functions. Xenopus oocytes have great advantages in the studies about the interactions between molecules and functional proteins, especially in the case when the expression levels of the proteins are small in cell culture systems without transfections. These are also proper to study the mechanisms underlying the interactions. Based on the data collected in Xenopus oocyte experiments, we can proceed to the next step, i.e., the physiological roles of the compounds and their significances. In the case of EAAT2, the effects on the neurotransmission should be examined by electrophysiological approach using acute brain slices. For new drug development, pharmacokinetics pharmacodynamics (PKPD) data and blood brain barrier (BBB) penetration data are also necessary. In order not to miss the promising candidate compounds at the primary stages of drug development, we should reconsider using Xenopus oocytes in the early phase of drug development.

The use of implementation science theories, models, and frameworks in implementation research for medicinal products: A scoping review

BackgroundThe uptake, adoption and integration of new medicines and treatment regimens within healthcare delivery can take a decade or more. Increasingly, implementation science (IS) research is being used to bridge this gap between the availability of new therapeutic evidence and its actual application in clinical practice. Little is known, however, about the quality of IS research in this area, including the degree to which theories, models and frameworks (TMFs) are being used. The objective of this study was to conduct a scoping review of the use of TMFs in implementation research involving medicinal products.MethodsA search was conducted for English language abstracts and manuscripts describing the application of TMFs in IS studies for medicinal products. Eligible publications were those published between 1 January 1974 and 12 December 2022. All records were screened at the title and abstract stage; included full-text papers were abstracted using data extraction tables designed for the study. Study quality was appraised using the Implementation Research Development Tool.ResultsThe initial scoping search identified 2697 publications, of which 9 were ultimately eligible for inclusion in the review. Most studies were published after 2020 and varied in their objectives, design and therapeutic area. Most studies had sample sizes of fewer than 50 participants, and all focused on the post-marketing phase of drug development. The TMF most frequently used was the Consolidated Framework for Implementation Research (CFIR). Although most studies applied all TMF domains, TMF use was limited to instrument development and/or qualitative analysis. Quality appraisals indicated the need for engaging patients and other stakeholders in the implementation research, reporting on the cost of implementation strategies, and evaluating the unintended consequences of implementation efforts.ConclusionsWe found that few IS studies involving medicinal products reported using TMFs. Those that did encompassed a wide variety of therapeutic indications and medicinal products; all were in the post-marketing phase and involved limited application of the TMFs. Researchers should consider conducting IS in earlier phases of drug development and integrating the TMFs throughout the research process. More consistent and in-depth use of TMFs may help advance research in this area.

Drug development for major chronic health conditions—aligning with growing public health needs: Proceedings from a multistakeholder think tank

The global pharmaceutical industry portfolio is skewed towards cancer and rare diseases due to more predictable development pathways and financial incentives. In contrast, drug development for major chronic health conditions that are responsible for a large part of mortality and disability worldwide is stalled. To examine the processes of novel drug development for common chronic health conditions, a multistakeholder Think Tank meeting, including thought leaders from academia, clinical practice, non-profit healthcare organizations, the pharmaceutical industry, the Food and Drug Administration (FDA), payors as well as investors, was convened in July 2022. Herein, we summarize the proceedings of this meeting, including an overview of the current state of drug development for chronic health conditions and key barriers that were identified. Six major action items were formulated to accelerate drug development for chronic diseases, with a focus on improving the efficiency of clinical trials and rapid implementation of evidence into clinical practice.

Patients with Obesity Should be Recognised as a Special Patient Population During Drug Development of Antibacterial and Antifungal Agents; A Call to Action.

Individuals with obesity are at increased risk of developing infectious diseases. Timely administration of an effective dose of an antimicrobial agent is paramount to safeguard optimal therapy. For this purpose, special patient populations at risk for altered exposure such as renal or hepatic impairment are studied during drug development. Strikingly, there is no such evaluation in individuals with obesity despite a potential influence on exposure and a global obesity prevalence of 13 %. Optimal clinical decision making in patients with obesity is impossible without prior study of the drug of interest in this population. This statement is strengthened by an evaluation of 19 antimicrobial agents that showed tremendous variability in the influence of weight on clearance. In contrast to patient with renal or hepatic impairment who are mainly at risk of overexposure, individuals with obesity can be at risk of both under- and overexposure. Gaining knowledge on the influence of body weight on clearance during early phases of drug development may allow for optimisation of other phases of research, potentially increasing success rate of the drug, and can provide clinicians with vital information as soon as the drug reaches the market. Antimicrobial therapy should be tailored to obesity-related (patho)physiological changes and to reach this goal, obese individuals should be studied during drug development.

SLIC-Occ: functional segmentation of occupancy images improves precision of EC50 images

BackgroundDrug occupancy studies with positron emission tomography imaging are used routinely in early phase drug development trials. Recently, our group introduced the Lassen Plot Filter, an extended version of the standard Lassen plot to estimate voxel-level occupancy images. Occupancy images can be used to create an EC50 image by applying an Emax model at each voxel. Our goal was to apply functional clustering of occupancy images via a clustering algorithm and produce a more precise EC50 image while maintaining accuracy.MethodA digital brain phantom was used to create 10 occupancy images (corresponding to 10 different plasma concentrations of drug) that correspond to a ground truth EC50 image containing two bilateral local “hot spots” of high EC50 (region-1: 25; region-2: 50; background: 6–10 ng/mL). Maximum occupancy was specified as 0.85. An established noise model was applied to the simulated occupancy images and the images were smoothed. Simple Linear Iterative Clustering, an existing k-means clustering algorithm, was modified to segment a series of occupancy images into K clusters (which we call “SLIC-Occ”). EC50 images were estimated by nonlinear estimation at each cluster (post SLIC-Occ) and voxel (no clustering). Coefficient of variation images were estimated at each cluster and voxel, respectively. The same process was also applied to human occupancy data produced for a previously published study.ResultsVariability in EC50 estimates was reduced by more than 80% in the phantom data after application of SLIC-Occ to occupancy images with only minimal loss of accuracy. A similar, but more modest improvement was achieved in variability when SLIC-Occ was applied to human occupancy images.ConclusionsOur results suggest that functional segmentation of occupancy images via SLIC-Occ could produce more precise EC50 images and improve our ability to identify local “hot spots” of high effective affinity of a drug for its target(s).

Inflammatory Response to Intravenously Administered Lipopolysaccharide (LPS) in the Bone Marrow of Healthy Volunteers As a Model for Early Phase Drug Development

Introduction Inflammatory responses in the peripheral and central hematological compartments are important for identification of new biomarkers and drug targets in early phase drug development. There is an increasing interest to investigate the modulation of the inflammatory response upon experimental drug exposure using cellular and molecular analyses, specifically in the central compartment. The lipopolysaccharide (LPS) challenge model allows to study TLR4-mediated local and systemic inflammation and therefore the effects of anti-inflammatory compounds. In addition, there is an interest in the effects of TLR4 agonists on early immune cell populations. The aim of the current study is to investigate the effects of intravenous (i.v.) LPS on the peripheral blood and central bone marrow compartment and to perform a proof-of-methodology study for bone marrow analysis in early phase drug research in healthy volunteers. Methods This was an open-label study in 13 healthy young male subjects, aged 18 - 35 years (inclusive). The study was conducted at the Centre for Human Drug Research, Leiden, The Netherlands. The study protocol was approved by the Medical Ethics Committee. All subjects provided written informed consent prior to any study activity. Subjects were divided into control group (n=3), 1 ng/kg i.v. LPS group (n=5) and 2 ng/kg i.v. LPS group (n=5). Blood and bone marrow samples were collected at baseline and at 4h post-dose timepoints. Primary objective was to evaluate the safety and tolerability of bone marrow sampling in healthy volunteers, by continuous safety monitoring, visual analogue scale (VAS) for pain and subject experience questionnaires. To investigate the pharmacodynamic (PD) effects of i.v. LPS challenge on the bone marrow and whole-blood immune compartments, flow cytometry, cytokine analysis and RNA sequencing was performed. Results All 13 subjects completed the study. Adverse events were mild and reversible, and included puncture site pain related to bone marrow puncture, and flu-like symptoms related to LPS administration. Twelve subjects (92%) indicated they would undergo the procedure again in the future. There was an observable difference in VAS pain score between the baseline bone marrow puncture and LPS treatment groups, but no difference between 1 ng/kg and 2 ng/kg groups. Phenotypic changes of inflammatory signatures in peripheral blood and bone marrow cell populations upon LPS administration were detected by multi-parameter flow cytometry. An LPS-induced inflammatory cytokine profile was present. RNA sequencing revealed differential gene expression in inflammatory gene signatures between peripheral blood mononuclear cells and bone marrow cells. Conclusion We demonstrated that bone marrow analysis is feasible and well-tolerable in early phase studies with healthy volunteers and can be used to assess inflammatory responses in the central hematological compartment upon systemic LPS challenge. Access to bone marrow can provide important novel insights into drug effects on the hematopoiesis and differentiation of immune cells and provides added value to early drug development.

The Validity of a Synthetic Control Arm Derived from Historical Multiple Myeloma Clinical Trials

Introduction While randomized phase III clinical trials (RCTs) are the gold-standard for establishing drug efficacy in the United States, external control arms (ECAs) may be valuable for both early and late phase drug development. An ECA is a collection of patients with the disease of interest who were treated outside of a clinical trial of interest (ie, target trial) and whose outcomes are compared to those of the target trial patients' to evaluate comparative drug efficacy and/or safety. ECAs created through rigorous statistical non-experimental methods such as matching patient-level characteristics may provide early estimates of comparative efficacy in advance of RCTs and offset prospective enrollment of control patients in randomized studies. We evaluate the validity of using a matched ECA to estimate the comparative efficacy of an experimental drug in relapsed refractory multiple myeloma (r/r MM). Methods From the anonymized Medidata Enterprise Data Store (MEDS), we identified historic RCTs in r/r MM containing patients exposed to an experimental drug as well as an active control drug (ie, a corticosteroid). Arbitrarily, we selected one trial to serve as the “target trial”. For the remaining trials, we collapsed all corticosteroid control patients into a single pool to construct an ECA. Using propensity score (PS) optimal full matching, we identified corticosteroid ECA patients by selecting from the candidate patients those whose baseline demographic and clinical attributes were similar to patients receiving the experimental drug in the target trial. Using the Kaplan-Meier (KM) estimator and a Cox proportional hazards regression, we first compared overall survival time (OS) of patients in the experimental drug arm vs control arm in the target trial. We then compared the OS in the experimental drug arm vs the ECA. Results We found fewer than five historic clinical trials from MEDS which were open-label RCTs conducted from 2010-2017. They enrolled r/r MM patients aged ≥18 years who had received more than two prior lines of therapy, one of which was lenalidomide and bortezomib. The target trial contained 300 patients in the investigational drug arm and 152 patients in the control arm. Using the PS matching, we identified 183 patients from the control arms in pooled trials that were matched with 290 patients in the investigational drug arm in the target trial (i.e., ECA). These ECA patients were then upweighted to be matched with 290 patients in the investigational drug arm in the target trial. Full matching successfully attenuated baseline differences between the investigational drug and ECA arms. Figure 2 shows the KM curves of OS (investigational vs control) in the target trial. Median OS was 13.25 months vs. 8.52 months (log-rank p&amp;lt;0.01) and the investigational drug was associated with a 36% reduction in the hazard of death (HR 0.74, 95% CI: 0.60-0.92). Figure 2 shows the KM curves of OS, after full matching, using the ECA. Median OS was 13.51 months vs. 8.75 months (log-rank p&amp;lt;0.01) and the investigational drug was associated with a 36% reduction in the hazard of death (HR 0.74, 95% CI: 0.58-0.94). Conclusions In this methodologic validation study, we have demonstrated that it is possible to produce an ECA from historical clinical trials data using propensity score methods which replicates the comparative efficacy estimate from a target without introducing bias. That is, the ECA, which was well balanced with the investigational arm with respect to baseline patient attributes, returned nearly identical estimates of comparative efficacy as assessed by OS of both target trial control arm and ECA against the target trial experimental arm. This work illustrates that it is possible to create a valid ECA even when historical data size is limited and to do so without excessive exclusion of non-matched patients. That the treatment effect on OS estimated in comparison to the randomized control was very closely matched by that of the ECA suggests this approach to ECAs may be used to (1) estimate comparative efficacy of therapies that have only been studied in single arm settings and/or (2) augment or replace a randomized control arm in future trials when ethically or practically challenging. Figure 1A. Kaplan-Meier OS estimates of target trial experimental (N=300) vs. control arm (N=152) Figure 1B. Kaplan-Meier OS estimates of target trial experimental (N=290) vs. ECA (N=290)

Estimation of target occupancy in repeated dosing design studies using positron emission tomography: Biases due to target upregulation.

Positron emission tomography (PET) has become indispensable in the quantification of target engagement by brain targeting medications. The relationship between the drug plasma concentration (or drug dose administered) and target occupancy determined during a PET occupancy study has provided valuable information for the assessment of novel pharmaceuticals in the early phases of drug development. Such information is also critical for the understanding of the mechanisms of action and side-effect profile of approved medication commonly used in the clinic. Occupancy studies conducted following repeated drug dosing (RD) can produce systematic differences from those conducted following single drug dose (SD), differences that have not been adequately explored. We have hypothesised that when differences are observed between RD and SD studies, they are related to changes in target density induced by repeated drug accumulation. We have developed a modified occupancy model to account for potential changes in target density and tested it on a sample dataset. We found that target upregulation can parsimoniously explain the differences in drug affinity estimated in SD and RD studies. Our findings have implications for the interpretation of RD occupancy data in the literature and the relationship between specific target occupancy levels and drug efficacy and tolerability.

Single-cell metabolic profiling reveals subgroups of primary human hepatocytes with heterogeneous responses to drug challenge

BackgroundXenobiotics are primarily metabolized by hepatocytes in the liver, and primary human hepatocytes are the gold standard model for the assessment of drug efficacy, safety, and toxicity in the early phases of drug development. Recent advances in single-cell genomics demonstrate liver zonation and ploidy as main drivers of cellular heterogeneity. However, little is known about the impact of hepatocyte specialization on liver function upon metabolic challenge, including hepatic metabolism, detoxification, and protein synthesis.ResultsHere, we investigate the metabolic capacity of individual human hepatocytes in vitro. We assess how chronic accumulation of lipids enhances cellular heterogeneity and impairs the metabolisms of drugs. Using a phenotyping five-probe cocktail, we identify four functional subgroups of hepatocytes responding differently to drug challenge and fatty acid accumulation. These four subgroups display differential gene expression profiles upon cocktail treatment and xenobiotic metabolism-related specialization. Notably, intracellular fat accumulation leads to increased transcriptional variability and diminishes the drug-related metabolic capacity of hepatocytes.ConclusionsOur results demonstrate that, upon a metabolic challenge such as exposure to drugs or intracellular fat accumulation, hepatocyte subgroups display different and heterogeneous transcriptional responses.

Efficient Drug Loading Method for Poorly Water-Soluble Drug into Bicelles through Passive Diffusion.

The bicelle, a type of solid lipid nanoparticle, comprises phospholipids with varying alkyl chain lengths and possesses the ability to solubilize poorly water-soluble drugs. Bicelle preparation is complicated and time-consuming because conventional drug-loading methods in bicelles require multiple rounds of thermal cycling or co-grinding with drugs and lipids. In this study, we proposed a simple drug-loading method for bicelles that utilizes passive diffusion. Drug-unloaded bicelles were placed inside a dialysis device and incubated in a saturated solution of ketoconazole (KTZ), which is a model drug. KTZ was successfully loaded into bare bicelles over time with morphological changes, and the final encapsulated concentration was dependent on the lipid concentration of the bicelles. When polyethylene glycol (PEG) chains of two different lengths (PEG2K and 5K) were incorporated into bicelles, PEG2k and PEG5k bicelles mitigated the morphological changes and improved the encapsulation rate. This mitigation of morphological changes enhanced the encapsulated drug concentration. Specifically, PEG5k bicelles, which exhibited the greatest prevention of morphological changes, had a lower encapsulated concentration after 24 h than that of PEG2k bicelles, indicating that PEGylation with a longer PEG chain length improved the loading capacity but decreased the encapsulation rate owing to the presence of a hydration layer of PEG. Thus, PEG with a certain length is more suitable for passive loading. Moreover, loading factors, such as temperature and vehicles used in the encapsulation process, affected the encapsulation rate of the drug. Taken together, the passive loading method offers high throughput with minimal resources, making it a potentially valuable approach during early drug development phases.

Inflammation across tissues: can shared cell biology help design smarter trials?

Immune-mediated inflammatory diseases (IMIDs) are responsible for substantial global disease burden and associated health-care costs. Traditional models of research and service delivery silo their management within organ-based medical disciplines. Very often patients with disease in one organ have comorbid involvement in another, suggesting shared pathogenic pathways. Moreover, different IMIDs are often treated with the same drugs (including glucocorticoids, immunoregulators and biologics). Unlocking the cellular basis of these diseases remains a major challenge, leading us to ask why, if these diseases have so much in common, they are not investigated in a common manner. A tissue-based, cellular understanding of inflammation might pave the way for cross-disease, cross-discipline basket trials (testing one drug across two or more diseases) to reduce the risk of failure of early-phase drug development in IMIDs. This new approach will enable rapid assessment of the efficacy of new therapeutic agents in cross-disease translational research in humans.

Characterization of Elimination Pathways and the Feasibility of Endogenous Metabolites as Biomarkers of Organic Anion Transporter 1/3 (Oat1/3) Inhibition in Cynomolgus Monkeys.

Organic anion transporters 1 and 3 (OAT1/3) occupy a key role in mediating renal elimination. Kynurenic acid (KYNA) was previously discovered as an effective endogenous biomarker to assess drug-drug interaction (DDI) for OAT inhibitors. Here, further in vitro and in vivo investigation was performed to characterize the elimination routes and feasibility of KYNA, along with other reported endogenous metabolites, as biomarkers of Oat1/3 inhibition in bile duct-cannulated (BDC) cynomolgus monkeys. Our results suggested KYNA is a substrate of OAT1/3 and OAT2, but not OCT2, MATE1/2K, or NTCP, and it shares comparable affinities between OAT1 and OAT3. Renal and biliary excretions, and plasma concentration-time profiles of KYNA, pyridoxic acid (PDA), homovanillic acid (HVA), and coproporphyrin I (CP-I), were assessed in BDC monkeys dosed with either probenecid (PROB) at 100 mg/kg or the control vehicle. Renal excretion of KYNA, PDA, and HVA was determined to be the major elimination route. The maximum plasma concentration (Cmax) and AUC0-24h of KYNA were about 11.6 and 3.7-fold higher in the PROB group than in the vehicle group. Renal clearance (CLr) of KYNA decreased by 3.2-fold, but biliary clearance (CLbile) was not altered after PROB administration. A similar trend was observed for PDA and HVA. Interestingly, an elevation of plasma concentration and reduction of CP-I CLbile were observed after PROB treatment, which suggested inhibition of the CP-I Oatp-Mrp2 transport axis by PROB. Overall, our results indicated that KYNA could potentially facilitate early and reliable assessment of DDI liabilities of Oat inhibition in monkeys. Significance Statement This work reported renal excretion as the major elimination pathway for kynurenic acid, pyridoxic acid, and homovanillic acid. Administration of probenecid reduced renal clearance and increased plasma exposure of these biomarkers in monkeys, consistent with the observation in humans. These endogenous biomarkers discovered in monkeys could be potentially used to evaluate the clinical drug-drug interactions (DDIs) in the early phase of drug development.

A win ratio-based framework to combine multiple clinical endpoints in exploratory basket trials

ABSTRACT In contemporary exploratory phase of oncology drug development, there has been an increasing interest in evaluating investigational drug or drug combination in multiple tumor indications in a single basket trial to expedite drug development. There has been extensive research on more efficiently borrowing information across tumor indications in early phase drug development including Bayesian hierarchical modeling and the pruning-and-pooling methods. Despite the fact that the Go/No-Go decision for subsequent Phase 2 or Phase 3 trial initiation is almost always a multi-facet consideration, the statistical literature of basket trial design and analysis has largely been limited to a single binary endpoint. In this paper we explore the application of considering clinical priorities of multiple endpoints based on matched win ratio to the basket trial design and analysis. The control arm data will be simulated for each tumor indication based on the corresponding null assumptions that could be heterogeneous across tumor indications. The matched win ratio matching on the tumor indication can be performed for individual tumor indication, pooled data, or the pooled data after pruning depending on whether an individual evaluation or a simple pooling or a pruning-and-pooling method is used. We conduct the simulation studies to evaluate the performance of proposed win ratio-based framework and the results suggest the proposed framework could provide desirable operating characteristics.

Evolution of Phase II Oncology Trial Design: from Single Arm to Master Protocol.

The recent development of novel anticancer treatments with diverse mechanisms of action has accelerated the detection of treatment candidates tremendously. The rapidly changing drug development landscapes and the high failure rates in Phase III trials both underscore the importance of more efficient and robust phase II designs. The goals of phase II oncology studies are to explore the preliminary efficacy and toxicity of the investigational product and to inform future drug development strategies such as go/no-go decisions for phase III development, or dose/indication selection. These complex purposes of phase II oncology designs call for efficient, flexible, and easy-to-implement clinical trial designs. Therefore, innovative adaptive study designs with the potential of improving the efficiency of the study, protecting patients, and improving the quality of information gained from trials have been commonly used in Phase II oncology studies. Although the value of adaptive clinical trial methods in early phase drug development is generally well accepted, there is no comprehensive review and guidance on adaptive design methods and their best practice for phase II oncology trials. In this paper, we review the recent development and evolution of phase II oncology design, including frequentist multistage design, Bayesian continuous monitoring, master protocol design, and innovative design methods for randomized phase II studies. The practical considerations and the implementation of these complex design methods are also discussed.

A review of challenges and prospects of 3D cell-based culture models used for studying drug induced liver injury during early phases of drug development.

Drug-induced liver injury (DILI) is the leading cause of compound attrition during drug development. Over the years, a battery of in-vitro cell culture toxicity tests is being conducted to evaluate the toxicity of compounds prior to testing in laboratory animals. Two-dimensional (2D) in-vitro cell culture models are commonly used and have provided a great deal of knowledge; however, these models often fall short in mimicking natural structures of tissues in-vivo. Testing in humans is the most logical method, but unfortunately there are ethical limitations associated with human tests. To overcome these limitations better human-relevant, predictive models are required. The past decade has witnessed significant efforts towards the development of three-dimensional (3D) in-vitro cell culture models better mimicking in-vivo physiology. 3D cell culture has advantages in being representative of the interactions of cells in-vivo and when validated can act as an interphase between 2D cell culture models and in-vivo animal models. The current review seeks to provide an overview of the challenges that make biomarkers used for detection of DILI not to be sensitive enough during drug development and explore how 3D cell culture models can be used to address the gap with the current models.

Gene Expression Profile as a Predictor of Seizure Liability

Analysis platforms to predict drug-induced seizure liability at an early phase of drug development would improve safety and reduce attrition and the high cost of drug development. We hypothesized that a drug-induced in vitro transcriptomics signature predicts its ictogenicity. We exposed rat cortical neuronal cultures to non-toxic concentrations of 34 compounds for 24 h; 11 were known to be ictogenic (tool compounds), 13 were associated with a high number of seizure-related adverse event reports in the clinical FDA Adverse Event Reporting System (FAERS) database and systematic literature search (FAERS-positive compounds), and 10 were known to be non-ictogenic (FAERS-negative compounds). The drug-induced gene expression profile was assessed from RNA-sequencing data. Transcriptomics profiles induced by the tool, FAERS-positive and FAERS-negative compounds, were compared using bioinformatics and machine learning. Of the 13 FAERS-positive compounds, 11 induced significant differential gene expression; 10 of the 11 showed an overall high similarity to the profile of at least one tool compound, correctly predicting the ictogenicity. Alikeness-% based on the number of the same differentially expressed genes correctly categorized 85%, the Gene Set Enrichment Analysis score correctly categorized 73%, and the machine-learning approach correctly categorized 91% of the FAERS-positive compounds with reported seizure liability currently in clinical use. Our data suggest that the drug-induced gene expression profile could be used as a predictive biomarker for seizure liability.

Interpretable Drug-to-Drug Network Features for Predicting Adverse Drug Reactions.

Recent years have witnessed booming data on drugs and their associated adverse drug reactions (ADRs). It was reported that these ADRs have resulted in a high hospitalisation rate worldwide. Therefore, a tremendous amount of research has been carried out to predict ADRs in the early phases of drug development, with the goal of reducing possible future risks. The pre-clinical and clinical phases of drug research can be time-consuming and cost-ineffective, so academics are looking forward to more extensive data mining and machine learning methods to be applied in this field of study. In this paper, we try to construct a drug-to-drug network based on non-clinical data sources. The network presents underlying relationships between drug pairs according to their common ADRs. Then, multiple node-level and graph-level network features are extracted from this network, e.g., weighted degree centrality, weighted PageRanks, etc. After concatenating the network features to the original drug features, they were fed into seven machine learning models, e.g., logistic regression, random forest, support vector machine, etc., and were compared to the baseline, where there were no network-based features considered. These experiments indicate that all the tested machine-learning methods would benefit from adding these network features. Among all these models, logistic regression (LR) had the highest mean AUROC score (82.1%) across all ADRs tested. Weighted degree centrality and weighted PageRanks were identified to be the most critical network features in the LR classifier. These pieces of evidence strongly indicate that the network approach can be vital in future ADR prediction, and this network-based approach could also be applied to other health informatics datasets.