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bioRxiv2026

Performance of the 10X Genomics Flex Single-Cell Sequencing Assay and its Application to Overcome Challenges in Clinical Trial Samples

BackgroundSingle-cell RNA sequencing (scRNA-seq) has become essential for understanding disease biology, yet its application in clinical trials is often limited by logistical challenges associated with the handling of biospecimens. Newly developed protocols aim to address these limitations by enabling profiling of fixed tissue. These new solutions need to be benchmarked against well-established protocols to assess their performance and suitability for clinical research. ResultsWe systematically compared different scRNA-seq protocols in a set of samples commonly analysed in clinical trials. The 10X Genomics GEM-X Flex Gene Expression assay (GEM-X Flex), in combination with the "chop-fix" preprocessing protocol, demonstrated superior performance to the standard GEM-X Universal Gene Expression solution when applied to both primary tumor tissue fragments and FFPE blocks. Moreover, the quality of the data obtained from GEM-X Flex applied to FFPE blocks outperformed that of single-nuclei RNA sequencing (snRNA-seq) from frozen biopsies, more robustly capturing the biological signals associated with the mechanism of action of a drug evaluated in an internal clinical trial. ConclusionsGEM-X Flex generates reliable, comprehensive transcriptomic data from both fixed tissue and clinical biopsies. By overcoming some of the limitations of fresh and frozen tissue analysis, this protocol offers a robust solution for the broad implementation of scRNA-seq in clinical trials.

genomics↗

bioRxiv2026

A Visually Interpretable Histopathology-Based Immune Model Predicts T-effector Biology and Response to Immune checkpoint inhibition in Clear Cell Renal Cell Carcinoma Clinical Trial and Contemporary Real-World Datasets

Immune checkpoint inhibitors (ICI) are central to the treatment of metastatic clear cell renal cell carcinoma (ccRCC), yet only a subset of patients derive durable benefit, and clinically deployable predictive biomarkers remain an unmet need. RNA-based T-effector signatures capture cytotoxic immune biology and have been associated with ICI response in clinical trial cohorts; however, their clinical implementation is limited by the marked spatial heterogeneity of ccRCC, as well as cost, long turnaround time, sample quality requirements, and limited accessibility. Here, we developed a visually interpretable deep learning (DL) model that predicts a T-cell-enriched immune score directly from hematoxylin and eosin (H&E)-stained whole-slide images. To overcome the inability of H&E morphology alone to distinguish lymphocyte subsets, we trained the model using multimodal spatial supervision from CD8, PAX8, and ERG IHC, which respectively identified cytotoxic T-cell-rich regions, tumor cells, and endothelial cells, thereby constraining immune predictions to relevant tumor microenvironmental niches. The resulting H&E DL Immune score was validated by pathologist review, comparison with held-out CD8 IHC annotations, and independent datasets. The H&E DL Immune score correlated with T-effector RNA scores across independent institutional and IMmotion150 clinical trial cohorts (spearman correlations of 0.726; p=5.90x10-15 and 0.706; p=4.04x10-19). As a proof of principle, the score was used to characterize associations with key biological features across large cohorts, including sarcomatoid differentiation, BAP1 and PBRM1 mutation status, and additional transcriptomic signatures. In IMmotion150 clinical trial cohort, a median-dichotomized H&E DL Immune score, similar to RNA-based T-effector score, was significantly associated with clinical benefit from atezulumab therapy. In contemporary institutional cohorts of patients treated with frontline ipilimumab plus nivolumab or in initial 3 lines of nivolumab monotherapy, patients in the top quartile of H&E DL Immune score had significantly longer progression-free survival. Collectively, these findings support a scalable and interpretable H&E-based biomarker that captures T-effector biology and can help identify patients with ccRCC more likely to benefit from ICIs.

bioinformatics↗

bioRxiv2024

Optimizing Neoadjuvant Treatment Response Prediction for Triple-Negative Breast Cancer Using Clinical Trial Data and Deep Auxiliary Learning

MotivationTriple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options and poor prognosis. Developing predictive models for TNBC treatment responses is crucial but challenging due to data scarcity and the reliance on cell line data, which limits clinical translational value. Leveraging omics data from clinical trials, particularly through auxiliary learning, offers a potential solution to enhance predictive accuracy and reduce data requirements. ResultsIn this study, we propose a new approach utilizing deep auxiliary task reweighting learning methods to automatically reweight auxiliary tasks, thereby optimizing the performance of the primary task of predicting TNBC treatment responses. We benchmark various auxiliary learning methods, including ARML, AdaLoss, GradNorm, and OL AUX, against traditional supervised machine learning algorithms and single-task learning baselines. Our results characterize the performance of auxiliary learning across various contexts, including utilizing parallel treatment arms within a multi-arm clinical trial, leveraging treatment arms from different clinical trials, and integrating multiple arms with the same treatment regimens across separate clinical trials. The last scenario also provides an opportunity for validating prediction models on an independent dataset, demonstrating the superior performance of the auxiliary learning models in predicting pathological complete response (pCR) in TNBC patients treated with standardized combinational chemotherapy with Taxane, Anthracycline, and Cyclophosphamide (TAC). Availability and ImplementationSource code and additional resources can be accessed at https://github.com/moonchangin/DeepAux TxPred TNBC.

bioinformatics↗

bioRxiv2018

Application of mechanistic methods to clinical trials in multiple sclerosis: the simvastatin case

The analysis of clinical trials is limited to pre-specified outcomes, thereby precluding a mechanistic understanding of the treatment response. Multivariate mechanistic models can elucidate the causal chain of events by simultaneous analysis of multimodal data that link intermediate variables to outcomes of interest. A double-blind, randomised, controlled, phase 2 clinical trial in secondary progressive multiple sclerosis (MS-STAT, NCT00647348) demonstrated that simvastatin (80mg/day) over two years reduced the brain atrophy rate and was associated with beneficial effects on cognitive and disability outcomes. Therefore, this trial offers an opportunity to apply mechanistic models to investigate the hypothesised pathways that link simvastatin to clinical outcome measures, either directly or indirectly via changes in serum total cholesterol levels and to determine which is the more likely.

neuroscience↗

bioRxiv2016

Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes

Despite many recent advances in systems biology and a marked increase in the availability of high-throughput biological data, the productivity of research and development in the pharmaceutical industry is on the decline. This is primarily due to clinical trial failure rates reaching up to 95% in oncology and other disease areas. We have developed a comprehensive analytical and computational pipeline utilizing deep learning techniques and novel systems biology analytical tools to predict the outcomes of phase I/II clinical trials. The pipeline predicts the side effects of a drug using deep neural networks and estimates drug-induced pathway activation. It then uses the predicted side effect probabilities and pathway activation scores as an input to train a classifier which predicts clinical trial outcomes. This classifier was trained on 577 transcriptomic datasets and has achieved a cross-validated accuracy of 0.83. When compared to a direct gene-based classifier, our multi-stage approach dramatically improves the accuracy of the predictions. The classifier was applied to a set of compounds currently present in the pipelines of several major pharmaceutical companies to highlight potential risks in their portfolios and estimate the fraction of clinical trials that were likely to fail in phase I and II.

bioinformatics↗

bioRxiv2026

Clinical Trial and Ontology-Derived Positive and Negative Benchmark Datasets for Drug Repurposing Across Rare Diseases

Evaluating the potential applications of a medicine is a fundamental challenge in drug development. There is a lack of standardized, decision-oriented benchmarks that test whether computational models can generalize therapeutic hypotheses across diseases in ways that reflect real-world pharmaceutical investment decision making. To address this gap, we introduce two complementary resources: the Indication Expansion Investment Decision Network (IxIDN) and the Orphanet Rare Disease Ontology Negative-network (ORDON). IxIDN is a clinical-trial-derived positive benchmark constructed by projecting drug-disease associations from pharmaceutical clinical trials into a disease-disease network; each edge connects disease pairs that have entered clinical trials for the same drug, thereby capturing cases when concrete indication-expansion decisions have been made. The current release contains 574 rare diseases and 5,336 edges. In contrast, ORDON serves as a stringent, biology-aware negative benchmark derived from the authoritative Orphanet Rare Disease Ontology. It identifies maximally distant disease pairs according to curated hierarchical structure and genetics-linked inheritance patterns, providing 793 rare diseases and 5,000 edges that represent high-separation negative candidates across therapeutic areas. Together, IxIDN and ORDON enable rigorous cross-evidence generalization from clinical trials to disease ontology, testing for Disease- Disease Association Learning (DDAL), a core task for mechanism-centered drug repurposing and indication expansion. All data are publicly available with detailed metadata, enabling reproducible evaluation of models on transparent, decision-relevant benchmarks.

Systems Biology↗

bioRxiv2024

An interim exploratory biomarker analysis of a Phase 2 clinical trial to assess the impact of CT1812 in Alzheimers disease

CT1812 is a novel, brain penetrant small molecule modulator of the sigma-2 receptor (S2R) that is currently in clinical development for the treatment of Alzheimers disease (AD). Preclinical and early clinical data show that, through S2R, CT1812 selectively prevents and displaces binding of amyloid beta (A{beta}) oligomers from neuronal synapses and improves cognitive function in animal models of AD. SHINE is an ongoing Phase 2 randomized, double-blind, placebo-controlled clinical trial (COG0201) in patients with mild to moderate AD, designed to assess the safety and efficacy of 6 months of CT1812 treatment. To elucidate the mechanism of action in AD patients and pharmacodynamic biomarkers of CT1812, the present study reports exploratory cerebrospinal fluid (CSF) biomarker data from an interim analysis of the first set of patients in SHINE (part A). Untargeted mass spectrometry-based discovery proteomics can detect more than 2,000 proteins in patient CSF and has documented utility in accelerating the identification of novel AD biomarkers reflective of diverse pathophysiologies beyond amyloid and tau and enabling identification of pharmacodynamic biomarkers in longitudinal interventional trials. We leveraged this technique to analyze CSF samples taken at baseline and after 6 months of CT1812 treatment. Proteome-wide protein levels were detected using tandem mass tag-mass spectrometry (TMT-MS), change from baseline was calculated for each participant, and differential abundance analysis by treatment group was performed. This analysis revealed a set of proteins significantly impacted by CT1812, including pathway engagement biomarkers (i.e., biomarkers tied to S2R biology) and disease modification biomarkers (i.e., biomarkers with altered levels in AD vs. healthy control CSF but normalized by CT1812, and biomarkers correlated with favorable trends in ADAS-Cog11 scores). Brain network mapping, Gene Ontology, and pathway analyses revealed an impact of CT1812 on synapses, lipoprotein and amyloid beta biology, and neuroinflammation. Collectively, the findings highlight the utility of this method in pharmacodynamic biomarker identification and providing mechanistic insights for CT1812, which may facilitate the clinical development of CT1812 and enable appropriate pre-specification of biomarkers in upcoming clinical trials of CT1812. HIGHLIGHTSO_LIEffects of CT1812 on AD patients were investigated in a randomized Phase 2 clinical trial C_LIO_LIPharmacodynamic biomarkers of CT1812 were identified through unbiased analysis of proteomics quantitation data acquired using TMT Mass Spectrometry (TMT-MS) C_LIO_LICT1812 normalized a set of biomarkers altered in AD C_LIO_LIFindings provide proof of mechanism that CT1812 impacts synapse, inflammation, amyloid-related processes C_LI

neuroscience↗

bioRxiv2024

Predictive value of preclinical models for CAR-T cell therapy clinical trials: a systematic review and meta-analysis

Experimental mouse models are indispensable for the preclinical development of cancer immunotherapies, whereby complex interactions in the tumor microenvironment (TME) can be somewhat replicated. Despite the availability of diverse models, their predictive capacity for clinical outcomes remains largely unknown, posing a hurdle in the translation from preclinical to clinical success. This study systematically reviews and meta-analyzes clinical trials of chimeric antigen receptor (CAR-) T cell monotherapies with their corresponding preclinical studies. Adhering to PRISMA guidelines, a comprehensive search of PubMed and ClinicalTrials.gov was conducted, identifying 422 clinical trials and 3157 preclinical studies. From these, 105 clinical trials and 180 preclinical studies, accounting for 44 and 131 distinct CAR constructs, respectively, were included. Patient[s] responses varied based on the target antigen, expectedly with higher efficacy and toxicity rates in hematological cancers. Preclinical data analysis revealed homogenous and antigen-independent efficacy rates. Our analysis revealed that only 4 % (n = 12) of mouse studies used syngeneic models, highlighting their scarcity in research. Three logistic regression models were trained on CAR structures, tumor entities, and experimental settings to predict treatment outcomes. While the logistic regression model accurately predicted clinical outcomes based on clinical or preclinical features (Macro F1 and AUC > 0.8), it failed in predicting preclinical outcomes from preclinical features (Macro F1 < 0.5, AUC < 0.6), indicating that preclinical studies may be influenced by experimental factors not accounted for in the model. These findings underscore the need for better understanding the experimental factors enhancing the predictive accuracy of mouse models in preclinical settings.

cancer biology↗

bioRxiv2026

COSMOS: A FAIR-aligned infrastructure for clinical trial data validation, warehousing, and interactive discovery

1.SummaryThe Clinical Omics System for Metadata and Outcome Storage (COSMOS) is an open-source, FAIR- and GCP aligned clinical trial unit (CTU) infrastructure designed to streamline the transition of academic clinical and multi-omic trial datasets into curated, analysis-ready repositories within Secure Data Environments (SDEs). By integrating an automated Data Quality and Data Validation (DQ&DV) "Trust Layer" with a relational Structured Query Language (SQL) schema, COSMOS enables programmatic and interactive data access via R Shiny applications. Dynamic integration of omics and clinical data is achieved through analytical data structures (e.g. ExpressionSet objects) linked via relational database identifiers. This lowers technical barriers for researchers and promotes governed data reuse and secondary discovery. Availability and ImplementationCOSMOS is freely available under the GPL-3.0 license at https://github.com/at2e19/SCTU_COSMOS_DQDV_Shiny Supplementary information: Supplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

In Vitro Safety Clinical Trial of the Cardiac Liability of Hydroxychloroquine and Azithromycin as COVID19 Polytherapy

Despite global efforts, there are no effective FDA-approved medicines for the treatment of SARS-CoV-2 infection. Potential therapeutics focus on repurposed drugs, some with cardiac liabilities. Here we report on a preclinical drug screening platform, a cardiac microphysiological system (MPS), to assess cardiotoxicity associated with hydroxychloroquine (HCQ) and azithromycin (AZM) polytherapy in a mock clinical trial. The MPS contained human heart muscle derived from patient-specific induced pluripotent stem cells. The effect of drug response was measured using outputs that correlate with clinical measurements such as QT interval (action potential duration) and drug-biomarker pairing. Chronic exposure to HCQ alone elicited early afterdepolarizations (EADs) and increased QT interval from day 6 onwards. AZM alone elicited an increase in QT interval from day 7 onwards and arrhythmias were observed at days 8 and 10. Monotherapy results closely mimicked clinical trial outcomes. Upon chronic exposure to HCQ and AZM polytherapy, we observed an increase in QT interval on days 4-8.. Interestingly, a decrease in arrhythmias and instabilities was observed in polytherapy relative to monotherapy, in concordance with published clinical trials. Furthermore, biomarkers, most of them measurable in patients serum, were identified for negative effects of single drug or polytherapy on tissue contractile function, morphology, and antioxidant protection. The cardiac MPS can predict clinical arrhythmias associated with QT prolongation and rhythm instabilities. This high content system can help clinicians design their trials, rapidly project cardiac outcomes, and define new monitoring biomarkers to accelerate access of patients to safe COVID-19 therapeutics.

bioengineering↗

bioRxiv2021

Application of machine learning methods in clinical trials for precision medicine

ObjectiveA key component for precision medicine is a good prediction algorithm for patients response to treatments. We aim to implement machine learning (ML) algorithms into the response-adaptive randomization (RAR) design and improve the treatment outcomes. Materials and MethodsWe incorporated nine ML algorithms to model the relationship of patient responses and biomarkers in clinical trial design. Such a model predicted the response rate of each treatment for each new patient and provide guidance for treatment assignment. Realizing that no single method may fit all trials well, we also built an ensemble of these nine methods. We evaluated their performance through quantifying the benefits for trial participants, such as the overall response rate and the percentage of patients who receive their optimal treatments. ResultsSimulation studies showed that the adoption of ML methods resulted in more personalized optimal treatment assignments and higher overall response rates among trial participants. Compared with each individual ML method, the ensemble approach achieved the highest response rate and assigned the largest percentage of patients to their optimal treatments. For the real-world study, we successfully showed the potential improvements if the proposed design had been implemented in the study. ConclusionIn summary, the ML-based RAR design is a promising approach for assigning more patients to their personalized effective treatments, which makes the clinical trial more ethical and appealing. These features are especially desirable for late-stage cancer patients who have failed all the FDA-approved treatment options and only can get new treatments through clinical trials.

bioinformatics↗

bioRxiv2018

Commitments by the biopharmaceutical industry to clinical trials transparency: the evolving environment

BackgroundSponsors of clinical trials have ethical obligations to register protocols, to report study results and to comply with applicable legal requirements.\n\nObjectiveTo evaluate public commitments to trial disclosure and rates of disclosure by members and non-members of the European Federation of Pharmaceutical Industries and Associations (EFPIA) and/or the Pharmaceutical Research and Manufacturers of America (PhRMA).\n\nStudy selectionWebsites of the top 50 biopharmaceutical companies by 2015 sales were searched for statements relating to trial data disclosure. Disclosure of trial results completed by biopharmaceutical industry and non-industry sponsors of at least 30 trials (2006-2015) was assessed using TrialsTracker.\n\nFindingsAmong the top 50 companies, 30 were EFPIA/PhRMA members and 20 were non-members, of which 26 and none, respectively, had a statement on their website committing to the disclosure of trials data. Of 29 377 trials in TrialsTracker, 9511 were industry-sponsored (69 companies) and 19 866 were non-industry-sponsored (254 institutions). The overall mean disclosure rate was 55%, with higher rates for industry (74%) than for non-industry sponsors (46%). Of the 30 companies within the top 50 with data in TrialsTracker, the mean disclosure rate was 76% (77% for EFPIA/PhRMA members [n = 25] versus 67% for non-members [n = 5]).\n\nConclusionsMost of the top 50 biopharmaceutical companies have publicly committed to the disclosure of trial data. Industry sponsors have responded to the ethical and legal demands of trial disclosure to a greater extent than non-industry sponsors, and now disclose three quarters of their trials.

scientific communication and education↗

bioRxiv2018

Design principles for TB vaccines’ clinical trials based on spreading dynamics

Tuberculosis (TB) is one of the most complex diseases from the perspective of mathematical epidemiology. Individuals recently infected with the bacillus Mycobacterium tuberculosis can either develop TB directly in a matter of several weeks, or enter into an asymptomatic latent TB infection state (LTBI) that only occasionally derives into active disease, sometimes even decades after the infection event. The possible interruptions that a vaccine might provoke on these two mechanisms are indistinguishable in phase II clinical trials. In this work, we present a new methodology that allows differentiating vaccines that slow down the progression to disease from vaccines that prevent it. By introducing a stochastic framework for simulating synthetic clinical trials based on transmission models, we show how the method proposed here contributes both to reduce uncertainty in vaccine characterization and impact forecasts as well as to assist the design of clinical trials, improving their probabilities of success.

epidemiology↗

bioRxiv2025

N-Power AI: A Specialized Agent Framework for Automated Sample Size and Power Analysis in Clinical Trial Design

BackgroundSample size and power analysis are essential in biomedical research and investigations, particularly in clinical trial design, as they ensure sufficient statistical power to detect meaningful effects. However, the complexity of these calculations often requires specialized statistical expertise, making the process inconvenient and limiting accessibility for researchers during early-stage study planning. MethodsWe developed N-Power AI, an agentic framework leveraging large language models (LLMs) to perform sample size and power calculations across diverse study designs. The framework consists of three specialized agents: the Function Agent, a perception and reasoning module that identifies appropriate statistical tests and corresponding R functions; the Calculation Agent, an action module that extracts parameters and executes precise computations; and the Reporting Agent, a presentation module that generates comprehensive, downloadable reports. N-Power AI and advanced LLMs (e.g., GPT-o1, Claude 3.5, Gemini 1.5 Pro) were evaluated against ground truths from statistical software (R) across six common clinical trial scenarios. ResultsDirect LLM outputs showed significant deviations from ground-truth values, particularly in complex scenarios like the Chi-Square Test and Cox Proportional Hazards Model. In contrast, N-Power AI achieved 100% agreement with ground truths across all scenarios. This accuracy is attributed to the Function Agents correct selection of statistical methods, the Calculation Agents accurate computations, and the Reporting Agents ability to produce clear and comprehensive summaries. ConclusionN-Power AI automates sample size and power analysis, offering an effective, efficient, and accessible solution for early-stage study planning. While human expertise remains crucial for high-level statistical planning, N-Power AI enhances accessibility and efficiency, streamlining the analysis process to generate reliable and reproducible results for a wide range of research scenarios.

bioinformatics↗

bioRxiv2026

CancerStop.dev: An Interactive Web Platform Integrating Prognostic Data, Clinical Trials, and Genomic Resources for Patient Empowerment

Patients facing cancer diagnoses must navigate fragmented information spanning prognosis, clinical research opportunities, and genomics-informed therapy. CancerStop.dev is a React-based web platform that consolidates trusted public resources into a single patient-centered interface to support informed decision-making. The platforms built-in ComboReg module presents interactive relative survival estimates up to 10 years after diagnosis by modeling (combined regression) age-at-diagnosis and stage-at-diagnosis using publicly available data from the SEER Program. Users can adjust an age slider and view stage-specific curves, enabling individualized and comprehensible visualizations of survivorship trends. A Clinical Trials module links directly to ClinicalTrials.gov, providing context-aware queries and free-form keyword filtering (e.g., mutations, investigational agents) to surface ongoing studies. The Genes & More module connects to NCBI ClinVar for variant-level insights, facilitating precision medicine exploration when genetic testing results are available. An Approved Drugs module routes to the National Cancer Institute resources listing FDA-approved agents relevant to specific cancers, aiding therapy literacy. A curated search tool (PresciQure) complements these features by streamlining access to the biomedical literature, package inserts of FDA-approved drugs, and related oncology resources. The platform is non-prescriptive, emphasizes transparency regarding data provenance and limitations, and is positioned to incorporate additional cancer types, demographic stratifications, and multi-omic resources. CancerStop.dev aims to empower patients, caregivers, and clinicians with timely, integrated, and navigable information, thereby strengthening their advocacy and encouraging participation in research and precision care. SignificanceCancerStop.dev integrates prognosis, clinical trials, and genomic insights into a single, user-friendly platform, enabling patient and care teams to make faster, better-informed decisions.

cancer biology↗

bioRxiv2024

Virtual Clinical Trials of BMP4 Differentiation Therapy: Digital Twins to Aid Glioblastoma Trial Design

Glioma stem cells (GSCs) are considered a major driver of glioblastoma (GBM) progression and are highly resistant to standard cytotoxic treatments. BMP4 has been shown to drive differentiation of GSCs, increase sensitivity to radiotherapy, slow growth and increase survival times in animal models. To assess the potential of BMP4 as a differentiation therapy, we develop a mathematical model that describes the growth of a GBM tumor via a hierarchy of GSCs, progenitor cells and terminally differentiated cells. We parametrize our model using experimental data from twelve patient-derived GSC lines, on which we measured response to radiotherapy and population growth with and without exposure to BMP4. Cell lines were typically more sensitive to radiotherapy after two days of BMP4 treatment but population growth can either increase or decrease after seven days of exposure to BMP4. To identify key parameters that drive successful treatment we perform global sensitivity analysis which identifies key parameters for BMP4 efficacy including proliferation rate and self-renewal sensitivity of GSCs. We then compare two treatment schedules: a single dose of BMP4 at resection and continuous delivery of BMP4 from resection till the end of radiotherapy. Due to the short half-life of BMP4 and its synergy with radiotherapy, continuous delivery of BMP4 during radiotherapy is more effective than a single dose prior to radiotherapy. We then perform a series of virtual clinical trials, stratified by tumor proliferation rate and GSC self-renewal sensitivity, which allows us to estimate the probability of observing a successful early-phase clinical trial for various virtual patient cohorts. We find that trials that selected the subset of patients with more proliferative GBMs were more likely to lead to significant improvements in survival. SignificanceTargeting glioma stem cells with BMP4 provides a novel opportunity to shift the complex cellular ecosystem of gliomas to enhance treatment efficacy. Mathematical modelling can facilitate optimal patient tumor feature selection when designing successful clinical trials.

cancer biology↗

bioRxiv2019

Streamlining and Cycle Time Reduction of the Start-up Phase of Clinical Trials

ObjectiveThe start-up phase of a clinical trial (CT) plays a vital role in execution of novel drug development. Hence, this study aims to identify the factors responsible for delaying the CT start-up phase. Further, it focuses on streamlining and reducing the cycle time of the start-up phase of newly sponsored CTs. MethodologyThirteen sponsored CTs conducted (between 2016 and 2017) in clinical research department at King Fahad Medical City, Riyadh were analyzed to identify the data specific start-up metrics using Find an improvement area-Organize a team-Clarify current practices-Understand source of variation/problem-Select a Strategy-Plan-Do-Check-Act (FOCUS-PDCA) cycle. Five measures incorporated in the metrics were: date of initial contact with site to the signing of confidentiality agreement; date of receiving questionnaire from sponsor to date of its completion; time taken to review protocol and approve investigational drug service (IDS) form; time taken to review protocol and approve pharmacy and pathology and clinical laboratory medicine (PCLM) form and date of receipt of institutional review board (IRB) submission package to final IRB approval. Fishbone analysis was used to understand the potential causes of process variation. Mean time was calculated for each metrics prior to and post implementation of the intervention protocol to analyze and compare percentage reduction in the mean cycle time of CTs. ResultsOf the various potential factors of delay identified through Fishbone analysis, the two major ones were lack of well-defined timeline for approval and review of the study protocol; and inconsistent IRB meetings. Post introduction of the new intervention protocol, the entire CT lifecycle was reduced by 45.6% (24.8 weeks' vs 13 weeks, before and after intervention, respectively). ConclusionVaried factors are responsible for delay of the start-up phase of CTs and understanding the impact of each factor allows for optimization and faster execution of the start-up phase of CTs.

epidemiology↗

bioRxiv2019

A novel technology for objective, accurate and non-invasive early diagnosis and monitoring of Alzheimer’s disease in clinics and clinical trials

We have developed a novel medical image processing technology for objective, accurate, and non-invasive diagnosis, prognosis, and monitoring of Alzheimers disease using standard clinical brain MRI (magnetic resonance imaging) and basic clinical cognitive assessment. The technology is a robust, highly accurate, fully automated, high-throughput, cloud-based platform, which operates in a fully integrated and controlled environment. In diagnosis mode, our technology performs with 91% balanced accuracy on blind testing to diagnose the current neurocognitive status, and in prognosis mode or early detection at mild cognitive impairment (MCI) stage with 88% balanced accuracy on blind testing to predict progression from MCI to Alzheimers dementia (AD) within 5 years. Such prognostic capability is currently non-existent, even in specialty clinics and hospitals, a major factor in Alzheimers clinical trial failures. The algorithms diagnostic certainty precisely mirrors the diagnostic confidence of an expert cognitive neurologist for both MCI (Spearmans Rho = 1) and AD (Rho = 1). In addition to widespread clinical applications, this novel technology can enable correct patient selection and therapeutic effect monitoring in clinical trials of Alzheimers disease, the crucial elements to finding a cure.

neuroscience↗