Success Rate Of Clinical Trials Research Articles

The Discovery of the Potential Attractive Compounds of Bactrocera dorsalis (Hendel)

Bactrocera dorsalis (Hendel) (B. dorsalis) is an important agricultural invasive pest that causes significant economic losses in tropical and subtropical fruit and vegetable crops. In this study, the proteins related to the sense of smell and taste of B. dorsalis, such as OBP, PBP, OR, IR, SNMP and CSP, were screened based on B. dorsalis transcriptome data. By integrating the compounds that were reported to be attractive to B. dorsalis, similar compounds of hydrocarbon compounds were obtained. Molecular docking was used to predict the binding between the similar compounds and the OBP, PBP, OR, IR, SNMP and CSP proteins. Network pharmacology was used to screen the potentially attractive compounds, and ecological experiments with B. dorsalis were finally conducted to verify the effect of these potentially attractive compounds on B. dorsalis. The results showed that the G protein-coupled receptor [BR: KO04030] and ion channel [BR: KO04040] pathways were closely related to the odor tropism of B. dorsalis. A total of 84 compounds, such as mitemcinal, exemestane and midecamycin, have potential binding effects on the B. dorsalis odor receptor proteins. The results of the ecological experiments showed that 1 mg/mL and 0.1 mg/mL 19-norandrostenedione, 1 mg/mL progesterone compounds was significantly attractive to B. dorsalis males, while 0.1 mg/mL exemestane was significantly attractive to B. dorsalis females. In this study, network pharmacology technology was used to discover the potential attractive compounds for B. dorsalis, which is important for the development and subsequent prevention and control of B. dorsalis. It can provide a reference in improving the success rates of clinical trials of new pest control products and in reducing the time and cost of drug development.

User Centered Rare Disease Clinical Trial Knowledge Graph (RCTKG).

Drug development in rare diseases is challenging due to the limited availability of subjects with the diseases and recruiting from a small patient population. The high cost and low success rate of clinical trials motivate deliberate analysis of existing clinical trials to understand status of clinical development of orphan drugs and discover new insight for new trial. In this project, we aim to develop a user centered Rare disease based Clinical Trial Knowledge Graph (RCTKG) to integrate publicly available clinical trial data with rare diseases from the Genetic and Rare Disease (GARD) program in a semantic and standardized form for public use. To better serve and represent the interests of rare disease users, user stories were defined for three types of users, patients, healthcare providers and informaticians, to guide the RCTKG design in supporting the GARD program at NCATS/NIH and the broad clinical/research community in rare diseases.

Facts and Hopes on Biomarkers for Successful Early Clinical Immunotherapy Trials: Innovative Patient Enrichment Strategies.

Despite the clinical validation and unequivocal benefit to patients, the development of cancer immunotherapies is facing some key challenges and the attrition rate in early phases of development remains high. Identifying the appropriate patient population that would benefit most from the drug is on the critical path for successful clinical development. We believe that a systematic implementation of patient enrichment strategies early in the drug development process and trial design, is the basis for an innovative, more efficient, and leaner clinical development to achieve earlier a clear proof of concept or proof of failure. In this position article, we will describe and propose key considerations for the implementation of patient enrichment strategies as an opportunity to provide decision-enabling data earlier in the drug development process. We introduce an innovative multidimensional tool for immuno-oncology drug development that focuses on facilitating the identification and prioritization of enrichment-relevant biomarkers, based on the drug mechanism of action. To illustrate its utility, we discuss patient enrichment examples and use a case in the field of cancer immunotherapy, together with technical and regulatory considerations. Overall, we propose to implement fit for purpose enrichment strategies for all investigational drugs as early as possible in the development process. We believe that this will increase the success rate of immuno-oncology clinical trials, and eventually bring new and better medicines to patients faster.

The moderating effect of APOE E4 on the association of plasma biomarkers with markers of cognition and brain health in Alzheimer’s disease

AbstractBackgroundDue to low success rates in clinical trials for Alzheimer’s disease (AD), there is a need to apply precision medicine approaches, such as stratifying based on APOE genotype, in order to assess its effects on outcome measures. Herein, we aim to better understand how plasma biomarkers of AD and neurodegenerative pathology are associated with cognition and neurodegeneration in APOE E4 carriers compared to non‐carriers.MethodPatients from the Ontario Neurodegenerative Disease Research Initiative (ONDRI) diagnosed with AD were included (n = 126, age = 71.0±8.2, 55%M). Plasma concentrations of Aβ40 and Aβ42 (Aβ42/40 ratio), glial fibrillary acidic protein (GFAP), neurofilament light (NfL) and phosphorylated‐tau181 (p‐tau181) were measured using Simoa assays. Composite cognitive domain scores (attention & working memory, executive function, language, memory, and visuospatial function) were computed from a comprehensive neuropsychological assessment. Volumes of regional grey matter, ventricular cerebrospinal fluid, white matter hyperintensities, perivascular spaces, lacunes and strokes were extracted from 3T structural MRI sequences using the SABRE pipeline. Linear regression models with an interaction term, controlling for age, sex and education were used to test the moderating effect of the APOE E4 allele on the association of plasma biomarkers with cognitive and MRI variables. When interaction effects were significant, post‐hoc models stratified by APOE E4 carrier status and controlling for age, sex and education were assessed.ResultThe APOE E4 allele moderated the association of GFAP, NfL and p‐tau181 with memory. Further stratification revealed that higher levels of GFAP, NfL and p‐tau181 were all associated with worse memory only in APOE E4 carriers. APOE E4 was also found to moderate the association of GFAP and p‐tau181 with many imaging markers. Further stratification revealed that higher levels of GFAP were associated with increased white matter hyperintensities, ventricular expansion and grey matter atrophy (temporal lobe, occipital lobe, hippocampus, basal ganglia and thalamus) only in APOE E4 carriers. Higher levels of p‐tau181 were also associated with increased temporal, parietal, and hippocampal atrophy only in APOE E4 carriers.ConclusionPlasma biomarkers, especially GFAP and p‐tau181, appear to be significantly more predictive of memory deficits and brain neurodegeneration in APOE E4 carriers compared to non‐carriers in AD.

Drug Development for Leukemia : Comprehensive Analysis of Clinical Trial Success Rates Presented at the American Society Hematology (ASH) Annual Meetings

Introduction: Slow progress in drug development is an ongoing issue even more critical for oncologic drugs, which have lower likelihood for approval than other therapeutic areas. Early clinical trials are frequently conducted and few eventually lead to drug approvals. We analyzed success rates of anti-leukemic drugs presented at the annual ASH meetings to identify rates of progression to advanced development and factors associated with clinical trial (CT) success. Methods: Our eligibility criteria included ASH meetings abstracts reporting CT assessing investigational anti-leukemic drugs conducted in the United States from 2011 to 2015. We searched ClinicalTrials.gov, PubMed, and Food and Drug Administration (FDA) websites to track the transition at each clinical phase (from Phase 1 to Phase 3), regulatory history, and publication as full-length manuscripts. We assessed the success rate (number of CT approved by FDA divided by the total number of CT) by study, drug and outcomes features using chi-square or Fisher's exact test. To verify the correlation between the impact factor (IF) of the journal in which the CT was published with the success of the study, the FDA-approval status (No vs Yes) was considered ordinal variable and the Pearson correlation coefficient was calculated. Results: Among the 3,929 abstracts for leukemia reviewed, 488 were main CT reports. Based on our eligibility criteria, 235 abstracts were identified including Phase 1 (121), Phase 2 (100), and Phase 3 (14) CT. Of these CT abstracts, only 45 (19.1%) eventually led to FDA approval. Among the unsuccessful studies, 37 (15.7%) progressed to the next one or two phases, and 153 (65.1%) did not transition any phase forward. Abstracts in acute myeloid leukemia (AML, 47%) and chronic lymphocytic leukemia (CLL, 30.2%) were the most common, followed by acute lymphocytic leukemia (ALL, 15.6%) and chronic myeloid leukemia (CML, 6.5%). Investigational New Drug (IND) combined with FDA-approved regimens (41.7%) or as monotherapy (40.9%) were the most commonly investigated therapy modalities. Combinations of multiple IND (9.8%) and new uses for FDA-approved drugs or regimens (7.7%) were evaluated less frequently. The overall success rate of CT was 6%. Although CML and ALL were the least studied types of leukemia, they had the highest approval success rates, 47% and 24.4%, respectively (p<0.001). Monotherapy INDs were more frequently approved by FDA (32.3%) than when combined with FDA-approved drugs (11.2%, p<0.001). FDA approval was also more frequent for advanced CT phase (p<0.001) and more recent (by year) presentation of the abstract (p=0.008). Tumor status (untreated vs relapsed and/or refractory [R/R] vs untreated and R/R) and the age of the patients included in the CT (adults including older adults vs older adults only vs pediatric vs adults and pediatric) were not significantly associated with approval rates (p>0.05). CT including drugs and regimens that led to FDA approval were published in journals with higher impact factors (R=0.368, p=0.0002), such as Lancet Oncology (24.1%), Blood (17.2%), Lancet Haematology (13.8%), and New England Journal of Medicine (13.8%). Blood was the journal that published the highest number of unsuccessful CT (15.3%). During the study period, CT assessing Enasidenib as monotherapy for R/R AML with IDH2 mutation, Ponatinib for CML and Philadelphia chromosome (Ph+) ALL, and Venetoclax for R/R CLL transitioned directly from Phase 2 to FDA approval. Conclusion: CT success rates for hematologic malignancies improved modestly over the last years. The high investment in CT for AML and CLL management has not resulted in increased approval rates proportional to reported CT.

Special FDA designations for drug development: orphan, fast track, accelerated approval, priority review, and breakthrough therapy.

Over the past decades, US Congress enabled the US Food and Drug Administration (FDA) to facilitate and expedite drug development for serious conditions filling unmet medical needs with five special designations and review pathways: orphan, fast track, accelerated approval, priority review, and breakthrough therapy. This study reviews the FDA's five special designations for drug development regarding their safety, efficacy/clinical benefit, clinical trials, innovation, economic incentives, development timelines, and price. We conducted a keyword search to identify studies analyzing the impact of the FDA's special designations (orphan, fast track, accelerated approval, priority review, and breakthrough therapy) on the safety, efficacy/clinical benefit, trials, innovativeness, economic incentives, development times, and pricing of new drugs. Results were summarized in a narrative overview. Expedited approval reduces new drugs' time to market. However, faster drug development and regulatory review are associated with more unrecognized adverse events and post-marketing safety revisions. Clinical trials supporting special FDA approvals frequently use small, non-randomized, open-label designs. Required post-approval trials to monitor unknown adverse events are often delayed or not even initiated. Evidence suggests that drugs approved under special review pathways, marketed as "breakthroughs", are more innovative and deliver a higher clinical benefit than those receiving standard FDA approval. Special designations are an economically viable strategy for investors and pharmaceutical companies to develop drugs for rare diseases with unmet medical needs, due to financial incentives, expedited development timelines, higher clinical trial success rates, alongside greater prices. Nonetheless, patients, physicians, and insurers are concerned about spending money on drugs without a proven benefit or even on drugs that turn out to be ineffective. While European countries established performance- and financial-based managed entry agreements to account for this uncertainty in clinical trial evidence and cost-effectiveness, the pricing and reimbursement of these drugs remain largely unregulated in the US. Special FDA designations shorten clinical development and FDA approval times for new drugs treating rare and severe diseases with unmet medical needs. Special-designated drugs offer a greater clinical benefit to patients. However, physicians, patients, and insurers must be aware that special-designated drugs are often approved based on non-robust trials, associated with more unrecognized side effects, and sold for higher prices.

Mouse models of diabetes-related ulcers: a systematic review and network meta-analysis

Diabetic foot ulcers (DFUs) are a common complication of diabetes, associated with important morbidity. Appropriate animal models of DFUs may improve drug development, and subsequently the success rate of clinical trials. However, while many models have been proposed, they are extremely heterogeneous, and no standard has emerged. We thus propose a systematic review with a network meta-analysis (NMA) to gather direct and indirect evidence, and compare the different mouse models of diabetes-related ulcers. The systematic search was performed in Pubmed and Embase. The main outcomes were wound size measurement at days 3, 7, 11 and 15 (±1 day). The risk of bias and methodological quality of all included studies was assessed by using the Systematic Review Center for Laboratory animal Experimentation (SYRCLE) risk of bias tool. Meta-regressions were done on prespecified variables, including mouse strain, type of ulcer, sex, age, and use of a splint. We included 295 studies. Among all models, only db/db, ob/ob, streptozotocin (STZ), and STZ+high fat diet mice showed a significantly delayed wound healing, compared with controls, at each time point. Age, sex and ulcer type had influence on wound healing, although not at all time points. In conclusion, the db/db model is associated with the largest delay in wound healing The STZ model also exhibits significantly decreased wound healing. STZ+high fat diet and ob/ob mice may also be relevant models of diabetes-related ulcers, although the results rely on a more limited number of studies. This work was funded by the Agence Nationale de la Recherche (grant ANR-18-CE17-0017).

Comparative analysis between 2D and 3D colorectal cancer culture models for insights into cellular morphological and transcriptomic variations

Drug development is a time-consuming and expensive process, given the low success rate of clinical trials. Now, anticancer drug developments have shifted to three-dimensional (3D) models which are more likely to mimic tumor behavior compared to traditional two-dimensional (2D) cultures. A comparative study among different aspects was conducted between 2D and 3D cultures using colorectal cancer (CRC) cell lines, in addition, Formalin-Fixed Paraffin-Embedded (FFPE) block samples of patients with CRC were used for evaluation. Compared to the 2D culture, cells grown in 3D displayed significant (p < 0.01) differences in the pattern of cell proliferation over time, cell death phase profile, expression of tumorgenicity-related genes, and responsiveness to 5-fluorouracil, cisplatin, and doxorubicin. Epigenetically, 3D cultures and FFPE shared the same methylation pattern and microRNA expression, while 2D cells showed elevation in methylation rate and altered microRNA expression. Lastly, transcriptomic study depending on RNA sequencing and thorough bioinformatic analyses showed significant (p-adj < 0.05) dissimilarity in gene expression profile between 2D and 3D cultures involving thousands of genes (up/down-regulated) of multiple pathways for each cell line. Taken together, the study provides insights into variations in cellular morphologies between cells cultured in 2D and 3D models.

Excessive concentrations of kinase inhibitors in translational studies impede effective drug repurposing

Drug repositioning seeks to leverage existing clinical knowledge to identify alternative clinical settings for approved drugs. However, repositioning efforts fail to demonstrate improved success rates in late-stage clinical trials. Focusing on 11 approved kinase inhibitors that have been evaluated in 139 repositioning hypotheses, we use data mining to characterize the state of clinical repurposing. Then, using a simple experimental correction with human serum proteins in invitro pharmacodynamic assays, we develop a measurement of a drug's effective exposure. We show that this metric is remarkably predictive of clinical activity for a panel of five kinase inhibitors across 23 drug variant targets in leukemia. We then validate our model's performance in six other kinase inhibitors for two types of solid tumors: non-small cell lung cancer (NSCLC) and gastrointestinal stromal tumors (GISTs). Our approach presents a straightforward strategy to use existing clinical information and experimental systems to decrease the clinical failure rate in drug repurposing studies.

Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges.

By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the entire drug discovery process stands to undergo a profound transformation, offering a myriad of advantages. Foremost among these is the ability of AI to conduct swift and efficient screenings of expansive compound libraries, significantly augmenting the identification of potential drug candidates. Moreover, AI algorithms can prove instrumental in predicting the efficacy and safety profiles of candidate compounds, thus endowing invaluable insights and reducing reliance on extensive preclinical and clinical testing. This predictive capacity of AI has the potential to streamline the drug development pipeline and enhance the success rate of clinical trials, ultimately resulting in the emergence of more efficacious and safer therapeutic agents. However, the deployment of AI in drug discovery introduces certain challenges that warrant attention. A primary hurdle entails the imperative acquisition of high-quality and diverse data. Furthermore, ensuring the interpretability of AI models assumes critical importance in securing regulatory endorsement and cultivating trust within scientific and medical communities. Addressing ethical considerations, including data privacy and mitigating bias, represents an additional momentous challenge, requiring assiduous navigation. In this review, we provide an intricate and comprehensive overview of the multifaceted challenges intrinsic to conventional drug development paradigms, while simultaneously interrogating the efficacy of AI in effectively surmounting these formidable obstacles.

Predicting bioequivalence and developing dissolution bioequivalence safe space in vitro for warfarin using a Physiologically-Based pharmacokinetic absorption model

ObjectiveBioequivalence (BE) studies support the approval and clinical use of both new and generic drug products. Narrow therapeutic index (NTI) drugs have relatively high costs and low success rates of BE evaluation clinical trials as high-risk drugs. A physiologically-based pharmacokinetic (PBPK) model can be used to evaluate the BE of two preparations. MethodsThis study inputs the basic physical and chemical property parameters of warfarin sodium available at the present stage into GastroPlus™ software, and combined it with the Advanced Compartmental Absorption and Transit (ACAT™) model built into the software. The PBPK model of Chinese individuals taking 2.5 mg of warfarin sodium orally while fasted condition was developed using the disposal parameters calculated from the clinically measured PK data of the reference preparations. The model was tested using the PK data of other reference preparations and tested preparations from different domestic manufacturers. ResultsThe results revealed that at least 30% of drugs are released in 30 min under a pH of 4.5 condition, and at least 80% are released in 30 min under a pH of 6.8 condition, which can be used as bioequivalent dissolution limits under fasted conditions. The risk of BE failure in the fed condition will be significantly reduced for the clinical study on the BE of warfarin sodium, which is a NTI drug if the fasted condition is bioequivalent. ConclusionThe results revealed that the PBPK models were successfully developed for 2.5 mg of warfarin sodium tablets in Chinese individuals. Developing a PBPK model for NTI drugs based on in vitro dissolution data in software is a promising method for BE evaluation, which can provide great help for developing new drugs and the clinical trial research of BE of generic drugs.

Complexity-to-Diversity and Pseudo-Natural Product Strategies as Powerful Platforms for Deciphering Next-Generation Therapeutics.

Stereochemical and skeletal complexity are particularly important vis-à-vis the cross-talks between a small molecule and a complementary active site of a biological target. This intricate harmony is known to increase selectivity, reduce toxicity, and increase the success rate in clinical trials. Therefore, the development of novel strategies for establishing underrepresented chemical space that is rich in stereochemical and skeletal diversity is an important milestone in a drug discovery campaign. In this review, we discuss the evolution of interdisciplinary synthetic methodologies utilized in chemical biology and drug discovery that has revolutionized the discovery of first-in-class molecules over the last decade with an emphasis on complexity-to-diversity and pseudo-natural product strategies as a remarkable toolbox for deciphering next-generation therapeutics. We also report how these approaches dramatically revolutionized the discovery of novel chemical probes that target underrepresented biological space. We also highlight selected applications and discuss key opportunities offered by these tools and important synthetic strategies used for the construction of chemical spaces that are rich in skeletal and stereochemical diversity. We also provide insight on how the integration of these protocols has the promise of changing the drug discovery landscape.

Peptides of a Feather: How Computation Is Taking Peptide Therapeutics under Its Wing.

Leveraging computation in the development of peptide therapeutics has garnered increasing recognition as a valuable tool to generate novel therapeutics for disease-related targets. To this end, computation has transformed the field of peptide design through identifying novel therapeutics that exhibit enhanced pharmacokinetic properties and reduced toxicity. The process of in-silico peptide design involves the application of molecular docking, molecular dynamics simulations, and machine learning algorithms. Three primary approaches for peptide therapeutic design including structural-based, protein mimicry, and short motif design have been predominantly adopted. Despite the ongoing progress made in this field, there are still significant challenges pertaining to peptide design including: enhancing the accuracy of computational methods; improving the success rate of preclinical and clinical trials; and developing better strategies to predict pharmacokinetics and toxicity. In this review, we discuss past and present research pertaining to the design and development of in-silico peptide therapeutics in addition to highlighting the potential of computation and artificial intelligence in the future of disease therapeutics.

Factors Affecting Success of New Drug Clinical Trials.

Clinical trials are an essential process in the development of new drugs. In spite of time-consuming processes and high costs, the overall success rate of clinical trials is only 7.9%, which is a high risk for biopharmaceutical companies. However, despite these huge risks, research on finding factors affecting clinical trials to overcome and manage to risks has been insufficient. Considering these characteristics of the pharmaceutical industry, this study investigated the factors affecting the success of sponsor-initiated clinical trials. The success factors investigated were categorized into four factors: quality of clinical trials, speed of clinical trials, relationship type, and communication. Logistic regression was performed to measure each factor by analyzing 24,295 cases of Phase 1 to 4 trials from ClinicalTrials.gov. Because of the analysis, the factors affecting the success of the clinical trials were varied according to each clinical phase and the drug types: New Molecular Entity (NME)/Biologics, and the success ratio in the quality variable affected the overall clinical trial phases. Additionally, the experience, speed, relationship type, and communication variables were also found to be statistically significant for the success of each phase and drug type.

Development and Evaluation of a Simulation-Based Algorithm to Optimize the Planning of Interim Analyses for Clinical Trials in ALS.

Late-phase clinical trials for neurodegenerative diseases have a low probability of success. In this study, we introduce an algorithm that optimizes the planning of interim analyses for clinical trials in amyotrophic lateral sclerosis (ALS) to better use the time and resources available and minimize the exposure of patients to ineffective or harmful drugs. A simulation-based algorithm was developed to determine the optimal interim analysis scheme by integrating prior knowledge about the success rate of ALS clinical trials with drug-specific information obtained in early-phase studies. Interim analysis schemes were optimized by varying the number and timing of interim analyses, together with their decision rules about when to stop a trial. The algorithm was applied retrospectively to 3 clinical trials that investigated the efficacy of diaphragm pacing or ceftriaxone on survival in patients with ALS. Outcomes were additionally compared with conventional interim designs. We evaluated 183-1,351 unique interim analysis schemes for each trial. Application of the optimal designs correctly established lack of efficacy, would have concluded all studies 1.2-19.4 months earlier (reduction of 4.6%-57.7% in trial duration), and could have reduced the number of randomized patients by 1.7%-58.1%. By means of simulation, we illustrate the efficiency for other treatment scenarios. The optimized interim analysis schemes outperformed conventional interim designs in most scenarios. Our algorithm uses prior knowledge to determine the uncertainty of the expected treatment effect in ALS clinical trials and optimizes the planning of interim analyses. Improving futility monitoring in ALS could minimize the exposure of patients to ineffective or harmful treatments and result in significant ethical and efficiency gains.

A Human Ovarian Tumor &amp; Liver Organ-on-Chip for Simultaneous and More Predictive Toxo-Efficacy Assays

In oncology, the poor success rate of clinical trials is becoming increasingly evident due to the weak predictability of preclinical assays, which either do not recapitulate the complexity of human tissues (i.e., in vitro tests) or reveal species-specific outcomes (i.e., animal testing). Therefore, the development of novel approaches is fundamental for better evaluating novel anti-cancer treatments. Here, a multicompartmental organ-on-chip (OOC) platform was adopted to fluidically connect 3D ovarian cancer tissues to hepatic cellular models and resemble the systemic cisplatin administration for contemporarily investigating drug efficacy and hepatotoxic effects in a physiological context. Computational fluid dynamics was performed to impose capillary-like blood flows and predict cisplatin diffusion. After a cisplatin concentration screening using 2D/3D tissue models, cytotoxicity assays were conducted in the multicompartmental OOC and compared with static co-cultures and dynamic single-organ models. A linear decay of SKOV-3 ovarian cancer and HepG2 liver cell viability was observed with increasing cisplatin concentration. Furthermore, 3D ovarian cancer models showed higher drug resistance than the 2D model in static conditions. Most importantly, when compared to clinical therapy, the experimental approach combining 3D culture, fluid-dynamic conditions, and multi-organ connection displayed the most predictive toxicity and efficacy results, demonstrating that OOC-based approaches are reliable 3Rs alternatives in preclinic.

Deep learning-based risk prediction for interventional clinical trials based on protocol design: A retrospective study

Success rate of clinical trials (CTs) is low, with the protocol design itself being considered a major risk factor. We aimed to investigate the use of deep learning methods to predict the risk of CTs based on their protocols. Considering protocol changes and their final status, a retrospective risk assignment method was proposed to label CTs according to low, medium, and high risk levels. Then, transformer and graph neural networks were designed and combined in an ensemble model to learn to infer the ternary risk categories. The ensemble model achieved robust performance (area under the receiving operator characteristic curve [AUROC] of 0.8453 [95% confidence interval: 0.8409-0.8495]), similar to the individual architectures but significantly outperforming a baseline based on bag-of-words features (0.7548 [0.7493-0.7603] AUROC). We demonstrate the potential of deep learning in predicting the risk of CTs from their protocols, paving the way for customized risk mitigation strategies during protocol design.

Recommendations for robust and reproducible preclinical research in personalised medicine

BackgroundPersonalised medicine is a medical model that aims to provide tailor-made prevention and treatment strategies for defined groups of individuals. The concept brings new challenges to the translational step, both in clinical relevance and validity of models. We have developed a set of recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine.MethodsThese recommendations have been developed following four main steps: (1) a scoping review of the literature with a gap analysis, (2) working sessions with a wide range of experts in the field, (3) a consensus workshop, and (4) preparation of the final set of recommendations.ResultsDespite the progress in developing innovative and complex preclinical model systems, to date there are fundamental deficits in translational methods that prevent the further development of personalised medicine. The literature review highlighted five main gaps, relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. We identified five points of focus for the recommendations, based on the consensus reached during the consultation meetings: (1) clinically relevant translational research, (2) robust model development, (3) transparency and education, (4) revised regulation, and (5) interaction with clinical research and patient engagement. Here, we present a set of 15 recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine.ConclusionsAppropriate preclinical models should be an integral contributor to interventional clinical trial success rates, and predictive translational models are a fundamental requirement to realise the dream of personalised medicine. The implementation of these guidelines is ambitious, and it is only through the active involvement of all relevant stakeholders in this field that we will be able to make an impact and effectuate a change which will facilitate improved translation of personalised medicine in the future.

Adaptive design clinical trials: current status by disease and trial phase in various perspectives.

An adaptive design is a clinical trial design that allows for modification of a structured plan in a clinical trial based on data accumulated during pre-planned interim analyses. This flexible approach to clinical trial design improves the success rate of clinical trials while reducing time, cost, and sample size compared to conventional methods. The purpose of this study is to identify the current status of adaptive design and present key considerations for planning an appropriate adaptive design based on specific circumstances. We searched for clinical trials conducted between January 2006 to July 2021 in the Clinical Trials Registry (ClinicalTrials.gov) using keywords specified in the Food and Drug Administration Adaptive Design Clinical Trial Guidelines. In order to analyze the adaptive designs used in selected cases, we classified the results according to the phase of the clinical trial, type of indication, and the specific adaptation method employed. A total of 267 clinical trials were identified on ClinicalTrials.gov. Among them, 236 clinical trials actually applied adaptive designs and were classified according to phase, indication types, and adaptation methods. Adaptive designs were most frequently used in phase 2 clinical trials and oncology research. The most commonly used adaptation method was the adaptive treatment selection design. In the case of coronavirus disease 2019, the most frequently used designs were adaptive platform design and seamless design. Through this study, we expect to provide valuable insights and considerations for the implementation of adaptive design clinical trials in different diseases and stages.

Biofabrication of Modular Spheroids as Tumor-Scale Microenvironments for Drug Screening.

To streamline the drug discovery pipeline, there is a pressing need for preclinical models which replicate the complexity and scale of native tumors. While there have been advancements in the formation of microscale tumor units, these models are cell-line dependent, time-consuming and have not improved clinical trial success rates. In this study, two methods for generating 3D tumor microenvironments are compared, rapidly fabricated hydrogel microspheres and traditional cell-dense spheroids. These modules are then bioassembled into 3D printed thermoplastic scaffolds, using an automated biofabrication process, to form tumor-scale models. Modules are formed with SKOV3 and HFF cells as monocultures and cocultures, and the fabrication efficiency, cell architecture, and drug response profiles are characterized, both as single modules and as multimodular constructs. Cell-encapsulated Gel-MA microspheres are fabricated with high-reproducibility and dimensions necessary for automated tumor-scale bioassembly regardless of cell type, however, only cocultured spheroids form compact modules suitable for bioassembly. Chemosensitivity assays demonstrate the reduced potency of doxorubicin in coculture bioassembled constructs and a ≈five-fold increase in drug resistance of cocultured cells in 3D modules compared with 2D monolayers. This bioassembly system is efficient and tailorable so that a variety of relevant-sized tumor constructs could be developed to study tumorigenesis and modernize drug discovery.