Success Rate Of Trials Research Articles

Cohort-level patient-derived organoid assays and prediction of clinical trial outcomes in PDAC.

785 Background: Clinical trials are one of the major barriers in contemporary drug development. Improving the number of therapeutic solutions for patients whilst limiting the financial burden of healthcare systems will require dramatically improving clinical trial success rates. Functional assays based on patient-derived organoids (PDO) are a promising new tool for derisking clinical development of new therapies, but their use has been limited due to small cohort sizes and the absence of systematic validation studies. Methods: We have assembled a collection of 70 demographically relevant PDAC PDOs. Each PDO was screened with a 22-drug panel including standard of care, chemotherapies and targeted therapies. Using a best-in-class patient response prediction model we generate cohort scale estimations of overall response rates (ORR) and progression-free survival (PFS). The estimations are compared with data from published clinical trials to determine the validity of the approach. Results: The PDOs exhibit representative treatment histories in terms of number of lines of treatment (mean = 1.60) and treatment types (prior treatment types: folfirinox = 68.6%, gemcitabine = 50%). All the organoids’ mutational profile, past patient treatments and subsequent responses to future lines were analyzed and compared to the distributions observed in the clinic. The cohort-level drug efficacy on our PDO collection matches retrospective clinical trial ORRs reported in the literature. Furthermore, our preclinical assay predicts the relative performance of drugs in different clinical trial arms. Conclusions: We report that large-scale PDO-based assays predict clinical ORR and PFS with best-in-class accuracy. This work shows that well-characterized PDO collections can be used to improve the success rates of clinical studies for new potential treatments in PDAC.

Role of Computer-Aided Design and Development in Modern Pharmaceuticals

The integration of molecular biology and computational technologies has transformed modern drug discovery and development processes. Advanced computational methodologies, particularly artificial intelligence (AI) and machine learning (ML), have reshaped traditional drug development approaches through unprecedented access to ligand property data, target binding interactions, three-dimensional protein structures, and virtual libraries containing billions of drug-like molecules. AI and deep learning (DL) implementations have enhanced multiple stages of drug discovery, from target identification to lead optimization. These computational advances, backed by improved hardware capabilities and sophisticated algorithms, now enable targeting of previously "undruggable" proteins. This review presents modern computational approaches in pharmaceutical development, including strategies for challenging protein targets through covalent regulation, allosteric inhibition, protein-protein interaction modulation, and targeted protein degradation. AI-driven methods have accelerated drug discovery pipelines, reduced development costs, and improved clinical trial success rates. The transition from traditional broad-spectrum approaches to precision medicine, supported by computational tools, has enabled personalized therapeutic strategies. Current limitations in computer-aided drug design persist, yet the combination of computational predictions with experimental validation continues to advance therapeutic development. Recent developments in quantum computing and advanced neural networks promise to further enhance drug discovery efficiency and success rates in the coming decades.

Accelerated approvals: Early-phase success or premature authorization?

We compare the clinical trial success rates of products receiving US Food and Drug Administration (FDA) accelerated approval (AA) to those approved without using this pathway. Our findings raise important questions about how the AA pathway can best optimize early access to therapeutics that are ultimately proven safe and effective.

TarKG: a comprehensive biomedical knowledge graph for target discovery.

Target discovery is a crucial step in drug development, as it directly affects the success rate of clinical trials. Knowledge graphs (KGs) offer unique advantages in processing complex biological data and inferring new relationships. Existing biomedical KGs primarily focus on tasks such as drug repositioning and drug-target interactions, leaving a gap in the construction of KGs tailored for target discovery. We established a comprehensive biomedical KG focusing on target discovery, termed TarKG, by integrating seven existing biomedical KGs, nine public databases, and traditional Chinese medicine knowledge databases. TarKG consists of 1143313 entities and 32806467 relations across 15 entity categories and 171 relation types, all centered around 3 core entity types: Disease, Gene, and Compound. TarKG provides specialized knowledges for the core entities including chemical structures, protein sequences, or text descriptions. By using different KG embedding algorithms, we assessed the knowledge completion capabilities of TarKG, particularly for disease-target link prediction. In case studies, we further examined TarKG's ability to predict potential protein targets for Alzheimer's disease (AD) and to identify diseases potentially associated with the metallo-deubiquitinase CSN5, using literature analysis for validation. Furthermore, we provided a user-friendly web server (https://tarkg.ddtmlab.org) that enables users to perform knowledge retrieval and relation inference using TarKG. TarKG is accessible at https://tarkg.ddtmlab.org.

Changes in Early-Phase Clinical Trials in China During 2013-2022: A Review.

Clinical trials on innovative drugs in China have witnessed a stage of rapid development in recent years. The introduction of a number of government policies has stimulated enthusiasm for research and development in the pharmaceutical industry. We analyzed the data of the early-phase clinical trials registered in the Chinese Center for Drug Evaluation (CDE) from September 6, 2013, to December 31, 2022. All related registration information disclosed on the CDE website, including posted time, drug classification, dosing formula, indications, trial design, and trial status, were summarized and analyzed. A total of 5336 early-phase clinical trials were identified. The quantity and growth rate of early-phase clinical trials have increased substantially each year. Chemical drugs accounted for the largest proportion of early-phase clinical trials, although it dropped from 85.7% in 2013 to 59.5% in 2022. Moreover, the number of oncology drugs has increased yearly, accounting for 49.2% of all early-phase clinical trials in 2022. We can conclude that during 2013-2022, the development of early-phase clinical trials has progressed due to government support and advanced development techniques. To enhance the efficiency and success rate of early-phase clinical trials in the future, it is necessary to acquire advanced technical support and improve standardized supervision systems.

Glutaminase-1 inhibition alleviates senescence of Wharton's jelly-derived mesenchymal stem cells via senolysis.

Replicative senescence of mesenchymal stem cells (MSCs) caused by repeated cell culture undermines their potential as a cell therapy because of the reduction in their proliferation and therapeutic potential. Glutaminase-1 (GLS1) is reported to be involved in the survival of senescent cells, and inhibition of GLS1 alleviates age-related dysfunction via senescent cell removal. In the present study, we attempted to elucidate the association between MSC senescence and GLS1. We conducted in vitro and in vivo experiments to analyze the effect of GLS1 inhibition on senolysis and the therapeutic effects of MSCs. Inhibition of GLS1 in Wharton's jelly-derived MSCs (WJ-MSCs) reduced the expression of aging-related markers, such as p16, p21, and senescence-associated secretory phenotype genes, by senolysis. Replicative senescence-alleviated WJ-MSCs, which recovered after short-term treatment with bis-2-(5-phenylacetamido-1,2,4-thiadiazol-2-yl)ethyl sulfide 3 (BPTES), showed increased proliferation and therapeutic effects compared to those observed with senescent WJ-MSCs. Moreover, compared to senescent WJ-MSCs, replicative senescence-alleviated WJ-MSCs inhibited apoptosis in serum-starved C2C12 cells, enhanced muscle formation, and hindered apoptosis and fibrosis in mdx mice. These results imply that GLS1 inhibition can ameliorate the therapeutic effects of senescent WJ-MSCs in patients with muscle diseases such as Duchenne muscular dystrophy. In conclusion, GLS1 is a key factor in modulating the senescence mechanism of MSCs, and regulation of GLS1 may enhance the therapeutic effects of senescent MSCs, thereby increasing the success rate of clinical trials involving MSCs.

A novel Phase II single-arm hybrid design to minimize trial duration and enhance subsequent Phase III trial success rate

Many Phase III trials fail for lack of significant efficacy for the testing agents although their success has been demonstrated in preceding Phase II trials. One of the major reasons for this discordance is the use of different endpoints in Phase II and III trials, respectively. In oncology clinical trials, tumor response (surrogate endpoint) can be determined quickly whereas survival estimation (direct endpoint) requires a long period of follow-up. To better bridge the gap between two endpoints, we propose a novel two-stage single-arm hybrid design for Phase II trials whereby the percent of tumor size change is used as an initial screening to select potentially effective agents within a short time interval followed by a second screening stage where survival is estimated to confirm the efficacy of agents. This design can improve trial efficiency and reduce cost by early stopping the evaluation of an ineffective agent based on the low percent of tumor size change. The second survival endpoint screening will substantially increase the success rate of the follow-up Phase III trial by using the same endpoint. Simulation studies demonstrated that our novel design has improved in terms of trial efficiency, trial length, and the success rate of following Phase III trials.

AI-Mediated Methods For Cancer Treatment

The integration of Artificial Intelligence (AI) into cancer treatment represents a major advancement in diagnostic, prognostic, and therapeutic methods. This article explores how AI enhances drug response predictions, optimizes treatment regimens, and improves diagnostic imaging precision, leading to early and accurate tumor detection. AI accelerates drug discovery by identifying novel compounds, optimizing dosages, and repurposing existing drugs, thereby enhancing treatment development. In clinical trials, AI improves trial design, patient selection, and monitoring, increasing efficiency and success rates. Its application in medical imaging, including tumor localization and organ segmentation, provides detailed insights for precise diagnostic and treatment planning. Despite challenges such as data privacy and model transparency, AI's continuous learning and integration into personalized medicine highlight its transformative potential in cancer care. This convergence of AI and medical science promises to revolutionize oncology, improve patient outcomes, and tackle complex challenges.

Rates and Predictors of Pain Reduction With Intracranial Stimulation for Intractable Pain Disorders.

Intracranial modulation paradigms, namely deep brain stimulation (DBS) and motor cortex stimulation (MCS), have been used to treat intractable pain disorders. However, treatment efficacy remains heterogeneous, and factors associated with pain reduction are not completely understood. We performed an individual patient review of pain outcomes (visual analog scale, quality-of-life measures, complications, pulse generator implant rate, cessation of stimulation) after implantation of DBS or MCS devices. We evaluated 663 patients from 36 study groups and stratified outcomes by pain etiology and implantation targets. Included studies comprised primarily retrospective cohort studies. MCS patients had a similar externalized trial success rate compared with DBS patients (86% vs 81%; P = .16), whereas patients with peripheral pain had a higher trial success rate compared with patients with central pain (88% vs 79%; P = .004). Complication rates were similar for MCS and DBS patients (12% vs 15%; P = .79). Patients with peripheral pain had lower likelihood of device cessation compared with those with central pain (5.7% vs 10%; P = .03). Of all implanted patients, mean pain reduction at last follow-up was 45.8% (95% CI: 40.3-51.2) with a 31.2% (95% CI: 12.4-50.1) improvement in quality of life. No difference was seen between MCS patients (43.8%; 95% CI: 36.7-58.2) and DBS patients (48.6%; 95% CI: 39.2-58) or central (41.5%; 95% CI: 34.8-48.2) and peripheral (46.7%; 95% CI: 38.9-54.5) etiologies. Multivariate analysis identified the anterior cingulate cortex target to be associated with worse pain reduction, while postherpetic neuralgia was a positive prognostic factor. Both DBS and MCS have similar efficacy and complication rates in the treatment of intractable pain. Patients with central pain disorders tended to have lower trial success and higher rates of device cessation. Additional prognostic factors include anterior cingulate cortex targeting and postherpetic neuralgia diagnosis. These findings underscore intracranial neurostimulation as an important modality for treatment of intractable pain disorders.

Evaluation of the study of control arms in randomized clinical trials of cancer.

11023 Background: Randomized clinical trials (RCTs) in cancer typically compare experimental to control arm regimens. The choice of an appropriate control (ctrl) arm can have a major impact on the expected success of an RCT. The frequency with which cancer RCTs use control arms based on established dosing and scheduling protocols has not been described. Methods: The HemOnc knowledgebase (KB) was used to identify systemic anti-cancer therapy (SACT) regimen variants, defined as regimens with identical components that differ in dosing, scheduling, and/or route. Non-cancer, nonrandomized, and non-SACT studies were excluded. Study publication year was used to define standard variants as those evaluated in the experimental arm of a positive phase 3 RCT >1 year prior to publication as a control arm, regardless of cancer type or context of treatment. Study-variant dyads were evaluated to determine whether the variant was standard. Success rates of RCTs with standard vs non-standard control arms were evaluated with Fisher’s exact test. Results: 5221 studies were associated with ≥1 named variant in the HemOnc KB, as of 2024-02-06. After exclusions, there were 3511 study-variant dyads (2386 studies; 1714 variants). The 9 most common regimens are shown in the Table. The median (IQR) number of variants per regimen was 2 (1-3); carboplatin & paclitaxel (CP) had the most variants (n=33). Across all control arm study-variant dyads, 2228/3492 (64%) utilized non-standard variants. For example, 60/97 studies (62%) of docetaxel as control used the 75 mg/m2 q3wk variant after it was established in 2000, whereas n=27 others used 19 non-standard variants. Trials that used a standard control arm had a numerically higher success rate, 45% vs 42% (OR 1.10, 95% CI 0.92-1.32). Conclusions: Non-standard regimen variants are frequently used in cancer RCTs. Reasons for this could include toxicity, patient convenience, or emerging data from smaller studies that establish comparable efficacy or improved toxicity to standard variants. However, the a priori efficacy of non-standard control arms is less rigorously established, and trials with standard control arms might be more successful. Future directions include quantifying the granular differences between standard and non-standard variants and investigating whether certain types of non-standard variation (e.g. fewer cycles, lower doses) associate with trial success rates. [Table: see text]

Pharmacological evaluation of E2730, a novel selective uncompetitive GAT1 inhibitor, on epileptiform activities in resected brain tissues from human focal cortical dysplasia ex vivo

Focal cortical dysplasia (FCD) is an important etiology of focal epilepsy in children and adults. However, only a few preclinical models sufficiently reproduce the characteristic histopathologic features of FCD. To improve the success rate of clinical trials for antiseizure medications (ASMs) in patients with FCD, more human-relevant preclinical models are needed, and epileptic foci resected from patients are a powerful tool for this purpose. Here, we conducted ex vivo studies using epileptic foci resected from patients with FCD type II to evaluate the pharmacologic effects of the ASM candidate E2730, a selective uncompetitive inhibitor of γ-aminobutyric acid transporter 1. We used the same ex vivo assay system to assess carbamazepine (CBZ), an ASM often prescribed for focal epilepsy, as a reference. At the higher dose tested (200 µM), both E2730 and CBZ suppressed spontaneous epileptiform activities almost completely. At the lower dose (100 µM), CBZ reduced the area of brain tissue showing epileptiform activity, whereas E2730 significantly decreased the number of epileptiforms. These findings suggest that E2730—both as a single agent and in combination with CBZ—merits evaluation in clinical trials involving patients with FCD.

Predictors of successful trial of labor after cesarean section (TOLAC) in women with one prior transverse cesarean section at Tertiary Hospitals in northwest Ethiopia: a multicenter study

BackgroundTrials of labor after cesarean section is the preferred strategy to decrease the cesarean delivery rate and reducing complications associated with multiple cesarean sections. The success rate of trials of labor after cesarean section and associated factors have not been well documented in Ethiopia. Hence, this study was aimed to determine the success rate and factors associated with the trial of labor after one cesarean section in five Comprehensive Specialized Hospitals located in northwest Ethiopia.MethodsAn institutional-based cross-sectional study was conducted among 437 women who came for the trial of labor from December 1, 2021, to March 30, 2022. All women who fulfilled the eligibility criteria were included to this study. Data was collected using structured and pre-tested questionnaire. Then, the data was entered into Epi Data 4.6 software and exported to SPSS version 26 for analysis. To identify the variables influencing the outcome variable, bivariable and multivariable logistic regression analyses were conducted. The model’s fitness was checked using the Hosmer-Lemeshow goodness of fit test, and an adjusted odds ratio with a 95% confidence interval was used to declare the predictors that are significantly associated with TOLAC.ResultsThe success rate of the trial of labor after one cesarean section was 56.3% (95% CI, 51.3%, 61.2%). Maternal age ≥ 35 years (AOR: 3.3, 95% CI 1.2, 9.3), the fetal station at admission ≤ zero (AOR: 5. 6, 95% CI 3.3, 9.5), vaginal delivery before cesarean section (AOR: 1.9, 95% CI 1.2, 3.2), and successful vaginal birth after cesarean delivery (AOR 2.2, 95% CI 1.2, 4.1) were found to have a significant association with the success rate of trial of labor after cesarean section.ConclusionsIn this study, the success rate of the trial of labor after a cesarean section was low as compared to the ACOG guideline and other studies in different countries. Therefore, the clinicians ought to offer counsel during antenatal and intrapartum period, encourage the women to make informed decision on the mode of delivery, and the practitioners need to follow fetal and maternal conditions strictly to minimize adverse birth outcomes.

Improving Drug Trial Success Rates in Systemic Lupus Erythematosus: Endotyping-based Patient Stratification Could be the way Forward

Systemic lupus erythematosus (SLE) is a complex heterogeneous autoimmune disease with protean clinical manifestations and phenotypes. As a result, any candidate molecule aiming to modulate a particular pathobiological pathway would likely fail to demonstrate efficacy in patients with mixed phenotypes. The success of the belimumab trial and the recent anifrolumab trial in SLE have provided evidence that stratifying patients based on their underlying pathobiological mechanism (e.g., endotypes) can improve the chances of success in drug trials. Various approaches to endotyping have been proposed to stratify SLE patients, such as biomarker profiling, gene expression signature fingerprinting, utilising transcriptomics and other ‘omics’ techniques for patient stratification, and molecular characterisation in both human subjects and animal models of SLE. Besides stratification of SLE patients based upon endotyping, incorporating ‘theratyping’ (which refers to outliers in any failed drug trial who exhibit a positive response) would further ‘fine-tune’ the subgrouping with uniform underlying pathobiology. Using a 2-pronged approach of defining theratypes of pre-endotyped patients could fast-track drug discovery of drugs for SLE treatment. Considering the success of recent trials, there is compelling evidence that this approach can significantly increase the likelihood of success in drug trials and pave the way for more effective treatments for SLE.

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.

Application of multidisciplinary team conference for neuromodulation candidates facilitates patient selection and optimization.

Psychological evaluation is required by insurance companies in the United States prior to proceeding with a spinal cord stimulation or a dorsal root ganglion stimulation trial. Since January 2017, we implemented a Multidisciplinary Team Conference for Neuromodulation in our center to facilitate the collaboration between pain physicians and psychologists and to optimize screening of neuromodulation candidates. This study aims to report the impact of this team conference on improvement of neuromodulation outcome in our center. Appropriateness of neuromodulation were discussed in the team conference after initial visit with the pain specialist and psychological evaluation. For this study, we prospectively and retrospectively collected data on neuromodulation candidates who went through the team conference and those who did not as controls. We discussed 461 patients in the team conference sessions from January 2017 to July 2023. Out of these, a spinal cord stimulator or a dorsal root ganglion stimulator trial was performed in 164 patients with 80.5% (132 cases) trial success rate leading to 140 implants. Out of these implants, 26 (18.6%) explanted and 21 (15%) required revision in 41 (29.3%) patients. We performed neuraxial neuromodulation trial for 70 patients without going through the team conference from January 2016 to July 2023 with a trial success rate of 45.7% (32 cases). In this group, 7 (21.9%) and 6 (18.8%) patients underwent explant and revision. The differences between the groups were statistically significant for trial success rate (odds ratio of 4.9 with p-value of <0.01) but not for explant (odds ratio of 0.8 with p-value of 0.627) or revision (odds ratio of 0.8 with p-value of 0.595). Implementing Multidisciplinary Team Conference increased trial success rate in our center. Team conference provides therapeutic benefit for patients, and also provides the opportunity for an educational discussion for trainees.

Tapping into Efficient Learning: An Exploration of the Impact of Sequential Learning on Skill Gains and Learning Curves in Central Venous Catheterization Simulator Training.

Medical residents learn how to perform many complex procedures in a short amount of time. Sequential learning, or learning in stages, is a method applied to complex motor skills to increase skill acquisition and retention but has not been widely applied in simulation-based training (SBT). Central venous catheterization (CVC) training could benefit from the implementation of sequential learning. CVC is typically taught with task trainers such as the dynamic haptic robotic trainer (DHRT). This study aims to determine the impact of sequential learning on skill gains and learning curves in CVC SBT by implementing a sequential learning walkthrough into the DHRT. 103 medical residents participated in CVC training in 2021 and 2022. One group (N = 44) received training on the original DHRT system while the other group (N = 59) received training on the DHRTsequential with interactive videos and assessment activities. All residents were quantitatively assessed on (e.g. first trial success rate, distance to vein center, overall score) the DHRT or DHRTsequential systems. Residents in the DHRTsequential group exhibited a 3.58 times higher likelihood of successfully completing needle insertion on their first trial than those in the DHRT only group and required significantly fewer trials to reach a pre-defined mastery level of performance. The DHRTsequential group also had fewer significant learning curves compared to the DHRT only group. Implementing sequential learning into the DHRT system significantly benefitted CVC training by increasing the efficiency of initial skill gain, reducing the number of trials needed to complete training, and flattening the slope of the subsequent learning curve.

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.

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

BackgroundOver 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.ObjectivesThis 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.MethodsWe 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.ResultsExpedited 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.ConclusionSpecial 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.