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Results for “Clinical Trials”

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bioRxiv2016

Microarray analysis identifies malignant field signatures in biopsy samples at diagnosis predicting the likelihood of lethal disease in patients with localized Gleason 6 and 7 prostate cancer.

Overtreatment of early-stage low-risk prostate cancer patients represents a significant problem in disease management and has significant socio-economic implications. Development of genetic and molecular markers of clinically significant disease in patients diagnosed with low grade localized prostate cancer would have a major impact in disease management. A gene expression signature (GES) is reported for lethal prostate cancer in biopsy specimens obtained at the time of diagnosis from patients with Gleason 6 and Gleason 7 tumors in a Swedish watchful waiting cohort with up to 30 years follow-up. A 98-genes GES identified 89% and 100% of all death events 4 years after diagnosis in Gleason 7 and Gleason 6 patients, respectively; at 6 years follow-up, 83% and 100% of all deaths events were captured in Gleason 7 and Gleason 6 patients, respectively. Remarkably, the 98-genes GES appears to perform successfully in patients stratification with as little as 2% of cancer cells in a specimen, strongly indicating that it captures a malignant field effect in human prostates harboring cancer cells of different degrees of aggressiveness. In Gleason 6 and Gleason 7 tumors from prostate cancer patients of age 65 or younger, GES identified 86% of all death events during the entire follow-up period. In Gleason 6 and Gleason 7 tumors from prostate cancer patients of age 70 or younger, GES identified 90% of all death events 6 years after diagnosis. Classification performance of the reported in this study 98-genes GES of lethal prostate cancer appeared suitable to meet design and feasibility requirements of a prospective 4 to 6 years clinical trial, which is essential for regulatory approval of diagnostic and prognostic tests in clinical setting. Prospectively validated GES of lethal PC in biopsy specimens of Gleason 6 and Gleason 7 tumors will help physicians to identify, at the time of diagnosis, patients who should be considered for exclusion from active surveillance programs and who would most likely benefit from immediate curative interventions.

Genomics↗

bioRxiv2017

Development of a targeted sequencing approach to identify prognostic, predictive and diagnostic markers in paediatric solid tumours

The implementation of personalised medicine in childhood cancers has been limited by a lack of clinically validated multi-target sequencing approaches specific for paediatric solid tumours. In order to support innovative clinical trials in high-risk patients with unmet need, we have developed a clinically relevant targeted sequencing panel spanning 311 kb and comprising 78 genes involved in childhood cancers. A total of 132 samples were used for the validation of the panel, including Horizon Discovery cell blends (n=4), cell lines (n=15), formalin-fixed paraffin embedded (FFPE, n=83) and fresh frozen tissue (FF, n=30) patient samples. Cell blends containing known single nucleotide variants (SNVs, n=528) and small insertion-deletions (indels n=108) were used to define panel sensitivities of [≥]98% for SNVs and [≥]83% for indels [95% CI] and panel specificity of [≥]98% [95% CI] for SNVs. FFPE samples performed comparably to FF samples (n=15 paired). Of 95 well-characterised genetic abnormalities in 33 clinical specimens and 13 cell lines (including SNVs, indels, amplifications, rearrangements and chromosome losses), 94 (98.9%) were detected by our approach. We have validated a robust and practical methodology to guide clinical management of children with solid tumours based on their molecular profiles. Our work demonstrates the value of targeted gene sequencing in the development of precision medicine strategies in paediatric oncology.

genomics↗

bioRxiv2018

A Machine Learning Approach Predicts Tissue-Specific Drug Adverse Events

One of the main causes for failure in the drug development pipeline or withdrawal post approval is the unexpected occurrence of severe drug adverse events. Even though such events should be detected by in vitro, in vivo, and human trials, they continue to unexpectedly arise at different stages of drug development causing costly clinical trial failures and market withdrawal. Inspired by the \"moneyball\" approach used in baseball to integrate diverse features to predict player success, we hypothesized that a similar approach could leverage existing adverse event and tissue-specific toxicity data to learn how to predict adverse events. We introduce MAESTER, a data-driven machine learning approach that integrates information on a compounds structure, targets, and phenotypic effects with tissue-wide genomic profiling and our toxic target database to predict the probability of a compound presenting with different types of tissue-specific adverse events. When tested on 6 different types of adverse events MAESTER maintains a high accuracy, sensitivity, and specificity across both the training data and new test sets. Additionally, MAESTER scores could flag a number of drugs that were approved, but later withdrawn due to unknown adverse events - highlighting its potential to identify events missed by traditional methods. MAESTER can also be used to identify toxic targets for each tissue type. Overall MAESTER provides a broadly applicable framework to identify toxic targets and predict specific adverse events and can accelerate the drug development pipeline and drive the design of new safer compounds.

pharmacology and toxicology↗

bioRxiv2018

An ABCA4 loss-of-function mutation causes a canine form of Stargardt disease

Autosomal recessive retinal degenerative diseases cause visual impairment and blindness in humans and dogs. Currently, no standard treatment is available but pioneering gene therapy-based canine models have been instrumental for clinical trials in humans. To study a novel form of retinal degeneration in Labrador retriever dogs with clinical signs indicating cone and rod degeneration, we used whole-genome sequencing of an affected sib-pair and their unaffected parents. A frameshift insertion in the ATP binding cassette subfamily A member 4 (ABCA4) gene (c.4176insC), leading to a premature stop codon in exon 28 (p.F1393Lfs1395) was identified. In contrast to unaffected dogs, no full-length ABCA4 protein was detected in the retina of an affected dog. The ABCA4 gene encodes a membrane transporter protein localized in the outer segments of rod and cone photoreceptors. In humans, the ABCA4 gene is associated with Stargardt disease (STGD), an autosomal recessive retinal degeneration leading to central visual impairment. A hallmark of STGD is the accumulation of lipofuscin deposits in the retinal pigment epithelium. The discovery of a canine homozygous ABCA4 loss-of-function mutation may advance the development of dog as a large animal model for human STGD.\n\nAuthor summaryStargardt disease (STGD) is the most common inherited retinal disease causing visual impairment and blindness in children and young adults, affecting 1 in 8-10 thousand people. For other inherited retinal diseases, the dog has become an established comparative animal model, both for identifying the underlying genetic causes and for developing new treatment methods.\n\nTo date, there is no standard treatment for STGD and the mouse model is the only available animal model to study the disease. As a nocturnal animal, the morphology of the mouse eye differs from humans and therefore the mouse model is not ideal for developing methods for treatment. We have studied a novel form of retinal degeneration in Labrador retrievers showing clinical signs similar to human STGD. To investigate the genetic cause of the disease, we used whole-genome sequencing of a family quartet including two affected offspring and their unaffected parents. This led to the identification of a loss-of-function mutation in the ABCA4 gene. The findings of this study may enable the development of a canine model for human STGD.

genetics↗

bioRxiv2018

Comparing the efficacy of cancer therapies between subgroups in basket trials

An increase in the number of targeted anti-cancer drugs and growing genomic stratification of patients has led to the development of basket clinical trials in which a single drug is tested simultaneously in multiple tumor subtypes under a master protocol. Basket trials typically involve few patients per type, making it difficult to rigorously compare responses across types. We describe the use of permutation testing to analyze tumor volume changes and Progression Free Survival across subtypes in basket trials for neratinib, larotrectinib, pembrolizumab, and imatinib. Permutation testing is a complement to the standard Simons two-stage binomial approach and can test for differences among subgroups using empirical null distributions while controlling for multiple hypothesis testing. This approach uncovers examples of therapeutic benefit missed by a binomial test; in the case of the SUMMIT trial, our analysis identifies an overlooked opportunity for use of neratinib in lung cancers carrying ERBB2 Exon 20 mutations.

cancer biology↗

bioRxiv2017

ITHANET: Information and database community portal for haemoglobinopathies

Haemoglobinopathies are the commonest monogenic diseases, with millions of carriers and patients worldwide. Online resources for haemoglobinopathies are largely divided into specialised sites catering for patients, researchers and clinicians separately. However, the severity, ubiquity and surprising genetic complexity of the haemoglobinopathies call for an integrated website to serve as a free and comprehensive repository and tool for patients, scientists and health professionals alike. This paper presents the ITHANET community portal, an expanding resource for clinicians and researchers dealing with haemoglobinopathies. It integrates information on news, events, publications, clinical trials and haemoglobinopathy-related organisations and experts and, most importantly, databases of variations, epidemiology and diagnostic and clinical data. Specifically, ITHANET provides annotation for 2690 haemoglobinopathy-related variations, epidemiological data for more than 180 countries and information for more than 600 HPLC diagnostic reports. The ITHANET portal accepts and incorporates contributions to its content by local experts from any country in the world and is freely available for the public at http://www.ithanet.eu.

bioinformatics↗

bioRxiv2018

Lead-DBS v2: Toward a comprehensive pipeline for deep brain stimulation imaging

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural / functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patients preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the method of choice.\n\nThis work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.

neuroscience↗

bioRxiv2018

BET inhibition induces HEXIM1- and RAD51-dependent conflicts between transcription and replication

BET bromodomain proteins are epigenetic readers required for oncogenic transcription activities, and BET inhibitors have been rapidly advanced into clinical trials. Understanding the effects of BET inhibition on other nuclear processes such as DNA replication will be important for future clinical applications. Here we show that BET inhibition causes replication stress in cancer and non-cancer cells due to a rapid burst in global RNA synthesis and interference of transcription with replication. We identify BRD4 as the main BET inhibitor target in this process and provide evidence that BRD4 inhibition causes transcription-replication interference through release of P-TEFb from its inhibitor HEXIM1, promoting RNA Polymerase II phosphorylation. Unusually, BET inhibitor-induced transcription-replication interference does not activate the classic ATM/ATR-dependent DNA damage response. We show however that they promote foci formation of the homologous recombination factor RAD51. Both HEXIM1 and RAD51 are required for BET inhibitor-induced fork slowing, but rescuing fork slowing by HEXIM1 or RAD51 depletion activate a DNA damage response. Our data support a new mechanism where BRD4 inhibition slows replication and suppresses DNA damage through concerted action of transcription and homologous recombination machineries. They shed new light on the roles of DNA replication and recombination in the action of this new class of cancer drugs.

molecular biology↗

bioRxiv2017

Potential clinical benefits of CBD-rich Cannabis extracts over purified CBD in treatment-resistant epilepsy: observational data meta-analysis

Potential clinical benefits of CBD-rich Cannabis extracts over purified CBD in treatment-resistant epilepsy: observational data meta-analysis\n\nDifferent therapies involving cannabinoid compounds have become popular on the past few years, particularly the use of canabidiol (CBD) based products for the treatment of child refractory epilepsy. In this segment we highlight genetic disorders such as the Dravet Syndrome, which has received a lot of attention in Brazil. Our country has been giving visibility for this issue after the public discussion regarding the patient Anne Fischer, who benefited from a treatment with hemp based products, imported from the United States of America as a nutritional supplement, and still unregistered in Brazil. To this moment there is no cannabinoid based product registered for clinical indication for epilepsy, so patients have at their disposal products considered nutritional supplements, in general produced from a type of Cannabis known as \"hemp\", which are commercialized in Brazil through medical prescription. Despite several anecdotal evidences from patients and family members, until now there is no consensus on medical literature over the efficacy and safety of these products. Some observational studies are available on scientific literature, but there is still a scarcity of clinical studies conducted under the logic, rigor and organization necessary for clinical trial dedicated to the register of a pharmaceutical product. The objective of this paper is to describe the analysis of several observational clinical studies available on the literature regarding the treatment of child refractory epilepsy with cannabinoid based products. Beyond attempting to establish the safety and efficacy of such products, when possible, the present analysis also intended to investigate if there is enough evidence between the different aspects of safety and efficacy between CBD enriched extracts compared to purified CBD products. Results: a systematic search for papers in the \"PubMed\" search system with the words \"Dravet\", \"Lennox-Gastaut\" and \"epilepsy\" combined with the terms \"Cannabis\", \"cannabinoid\" and \"child\" yielded 30 papers. From those, 24 were not considered for the systematic review, for not having valid content (13), for being opinion only papers (6), showed not clinical data (4) and reported different subjects (1), resulting in 6 valid papers published between 2013 and 2016. One additional study was in press at the moment of the search was added manually (1) resulting in 7 valid references for analysis, with an average impact factor of 5,9 (2,3 to 21,8). Public data from partial reports of controlled randomized studies conducted in order to register a medication (2) were also considered, when appropriated and were mentioned in the text. The categorical data were analyzed by the Fischer test. Overall, the papers analyzed report observational clinical data of 442 patients, treated with CBD rich extracts or purified CBD, with he average daily dose between 1 and 50 mg/kg, with treatment length from 3 to 12 months (average of 6,2 months). A considerable amount of 66% (292/442) of the patients reported improvement in the frequency of convulsive crisis. There were more reports of improvement from patients treated with purified CBD (242/285) than patients treated with purified CBD (68/157), with statistical significance (p<0,0001). Nevertheless, when the standard clinical threshold of a \"50% reduction or more in the frequency of convulsive crisis\" was applied, only 40% of the individuals are considered respondent, and there were no difference (p=0,57) between the treatments with extract (64/168) and purified CBD (65/157). However, even that both treatments have similar efficacy, the patients treated with CBD enriched extracts reported a lower average dose than purified CBD patients. The average CBD equivalent dose on the extracts was 7,1 mg/kg/day, while the purified CBD was 22,9 mg/kg/day, suggesting that CBD is about 3x more potent in the extract than in its purified form. Looking only at the data relative to genetic originated disorders, there is evidence of a superior efficacy on Dravet Syndrome patients (37/72, p=0,01), but not for Lennox-Gastaut Syndrome (78/188, p=0,18), compared to the number of refractory epilepsy respondents in general (107/305). There is also an advantage of the CBD enriched extracts related to the occurrence of side effects. The report of mild side effects (109/285 vs. 291/346, p<0,0001) and severe (23/285 vs. 77/346, p<0,0001) are more frequent in products containing purified CBD than on CBD enriched extracts. Important to mention that these are the numbers of total reports of side effects, it is not possible to infer which fraction of these numbers are related to the treatment. In conclusion, this meta analysis suggests that treatments using CBD enriched extracts are more potent and have a better profile of adverse effects (but not more efficacy) than products containing purified CBD, at least in this population of patients with refractory epilepsy. The lack of standardization between extracts containing Cannabis does not allow us to infer directly which characteristics of the product that confer this therapeutic advantage, but it is likely related to other compounds present in the formulation that act sinergistically with CBD. Controlled studies with standardized Cannabis based extracts are necessary to confirm these observations.\n\n*Presented as an abstract and lecture to the 2017 CannMed event, at the Harvard Medical School, Boston, USA, in may 2017.

pharmacology and toxicology↗

bioRxiv2017

Heterogeneity in the tumour size dynamics differentiates Vemurafenib, Dabrafenib and Trametinib in metastatic melanoma

Molecular heterogeneity in tumours leads to variability in drug response both between patients and across lesions within a patient. These sources of variability could be explored through analysis of routinely collected clinical trial imaging data. We applied a mathematical model of tumour growth to analyse both within and between patient variability in tumour size dynamics to clinical data from three drugs, Vemurafenib, Dabrafenib and Trametinib, used in the treatment of metastatic melanoma. The analysis revealed: 1) existence of homogeneity in drug response and resistance development within a patient; 2) tumour shrinkage rate does not relate to rate of resistance development; 3) Vemurafenib and Dabrafenib, two BRAF inhibitors, have different variability in tumour shrinkage rates. Overall these results show how analysis of the dynamics of individual lesions can shed light on the within and between patient differences in tumour shrinkage and resistance rates, which could be used to gain a macroscopic understanding of tumour heterogeneity.

cancer biology↗

bioRxiv2018

Epilepsy gene therapy using non-integrating lentiviral delivery of an engineered potassium channel gene

Refractory focal neocortical epilepsy is a devastating disease for which there is frequently no effective treatment. Gene therapy represents a promising alternative, but treating epilepsy in this way involves irreversible changes to brain tissue, so vector design must be carefully optimized to guarantee safety without compromising efficacy. We set out to develop an epilepsy gene therapy vector optimized for clinical translation. The gene encoding the voltage-gated potassium channel Kv1.1, KCNA1, was codon-optimized for human expression and mutated to accelerate the channels recovery from inactivation. For improved safety, this engineered potassium channel (EKC) gene was packaged into a non-integrating lentiviral vector under the control of a cell type-specific CAMK2A promoter. In a blinded, randomized, placebo-controlled pre-clinical trial, the EKC lentivector robustly reduced seizure frequency in a rat model of focal neocortical epilepsy characterized by discrete spontaneous seizures. This demonstration of efficacy in a clinically relevant setting, combined with the improved safety conferred by cell type-specific expression and integration-deficient delivery, identify EKC gene therapy as ready for clinical translation in the treatment of refractory focal epilepsy.

neuroscience↗

bioRxiv2017

Translating GWAS Findings Into Therapies For Depression And Anxiety Disorders: Drug Repositioning Using Gene-Set Analyses And Testing For Enrichment Of Psychiatric Drug Classes

Depression and anxiety disorders are the first and sixth leading cause of disability worldwide according to latest reports from the World Health Organization. Despite their high prevalence and the significant disability resulted, there are limited advances in new drug development. On the other hand, the advent of genome-wide association studies (GWAS) has greatly improved our understanding of the genetic basis underlying psychiatric disorders.\n\nIn this work we employed gene-set analyses of GWAS summary statistics for drug repositioning. We explored five related GWAS datasets, including two on major depressive disorder (MDD-PGC and MDD-CONVERGE, with the latter focusing on severe melancholic depression), one on anxiety disorders, and two on depressive symptoms and neuroticism in the population. We extracted gene-sets associated with each drug from DSigDB and examined their association with each GWAS phenotype. We also performed repositioning analyses on meta-analyzed GWAS data, integrating evidence from all related phenotypes.\n\nImportantly, we showed that the repositioning hits are generally enriched for known psychiatric medications or those considered in clinical trials, except for MDD-PGC. Enrichment was seen for antidepressants and anxiolytics but also for antipsychotics. We also revealed new candidates or drug classes for repositioning, some of which were supported by experimental or clinical studies. For example, the top repositioning hit using meta-analyzed p-values was fendiline, which was shown to produce antidepressant-like effects in mouse models by inhibition of acid sphingomyelinase and reducing ceramide levels. Taken together, our findings suggest that human genomic data such as GWAS are useful in guiding drug discoveries for depression and anxiety disorders.

genetics↗

bioRxiv2018

Classes of ITD predict outcomes in patients with AML treated with FLT3 inhibitors

Recurrent internal tandem duplication (ITD) mutations are observed in various cancers including acute myeloid leukemia (AML). ITD mutations of Fms-like tyrosine kinase 3 (FLT3) receptor increase kinase activity, and are associated with poor prognostic outcomes. Currently, several small-molecule FLT3 inhibitors (FLT3i) are in clinical trials for targeted therapy of high-risk FLT3-ITD-positive AML. However, the variability of survival following FLT3i treatment suggests that the mere presence of FLT3-ITD mutations in a patient might not guarantee effective clinical response to targeted inhibition of FLT3 kinase. Motivated by the heterogeneity of FLT3-ITD mutations, we sought to investigate the effects of FLT3-ITD structural features on response to treatment in AML patients. To this end, we developed HeatITup (HEAT diffusion for Internal Tandem dUPlication), an algorithm to efficiently and accurately identify ITDs and classify them based on their nucleotide composition into newly defined categories of \"typical\" or \"atypical\". Typical ITDs insert sequences are entirely endogenous to the FLT3 locus whereas atypical ITDs contain nucleotides exogenous to the wildtype FLT3. We applied HeatITup to our cohort of de novo and relapsed AML patients. Individuals with AML carrying typical ITDs benefited significantly more from FLT3i than patients with atypical ITDs, regardless of whether FLT3i was used after initial induction or at relapse. Furthermore, analysis of the TCGA AML cohort demonstrated improved survival for patients with typical ITDs treated with induction chemotherapy. These results underscore the importance of structural discernment of complex somatic mutations such as ITDs in progressing towards personalized treatment for AML patients.

cancer biology↗

bioRxiv2017

A New Big-Data Paradigm For Target Identification And Drug Discovery

Drug target identification is one of the most important aspects of pre-clinical development yet it is also among the most complex, labor-intensive, and costly. This represents a major issue, as lack of proper target identification can be detrimental in determining the clinical application of a bioactive small molecule. To improve target identification, we developed BANDIT, a novel paradigm that integrates multiple data types within a Bayesian machine-learning framework to predict the targets and mechanisms for small molecules with unprecedented accuracy and versatility. Using only public data BANDIT achieved an accuracy of approximately 90% over 2000 different small molecules - substantially better than any other published target identification platform. We applied BANDIT to a library of small molecules with no known targets and generated [~]4,000 novel molecule-target predictions. From this set we identified and experimentally validated a set of novel microtubule inhibitors, including three with activity on cancer cells resistant to clinically used anti-microtubule therapies. We next applied BANDIT to ONC201 - an active anti- cancer small molecule in clinical development - whose target has remained elusive since its discovery in 2009. BANDIT identified dopamine receptor 2 as the unexpected target of ONC201, a prediction that we experimentally validated. Not only does this open the door for clinical trials focused on target-based selection of patient populations, but it also represents a novel way to target GPCRs in cancer. Additionally, BANDIT identified previously undocumented connections between approved drugs with disparate indications, shedding light onto previously unexplained clinical observations and suggesting new uses of marketed drugs. Overall, BANDIT represents an efficient and highly accurate platform that can be used as a resource to accelerate drug discovery and direct the clinical application of small molecule therapeutics with improved precision.

pharmacology and toxicology↗

bioRxiv2017

A machine learning approach to predicting short-term mortality risk in patients starting chemotherapy

BackgroundCancer patients who die soon after starting chemotherapy incur costs of treatment without benefits. Accurately predicting mortality risk from chemotherapy is important, but few patient data-driven tools exist. We sought to create and validate a machine learning model predicting mortality for patients starting new chemotherapy.\n\nMethodsWe obtained electronic health records for patients treated at a large cancer center (26,946 patients; 51,774 new regimens) over 2004-14, linked to Social Security data for date of death. The model was derived using 2004-11 data, and performance measured on non-overlapping 2012-14 data.\n\nFindings30-day mortality from chemotherapy start was 2.1%. Common cancers included breast (21.1%), colorectal (19.3%), and lung (18.0%). Model predictions were accurate for all patients (AUC 0.94). Predictions for patients starting palliative chemotherapy (46.6% of regimens), for whom prognosis is particularly important, remained highly accurate (AUC 0.92). To illustrate model discrimination, we ranked patients initiating palliative chemotherapy by model-predicted mortality risk, and calculated observed mortality by risk decile. 30-day mortality in the highest-risk decile was 22.6%; in the lowest-risk decile, no patients died. Predictions remained accurate across all primary cancers, stages, and chemotherapies--even for clinical trial regimens that first appeared in years after the model was trained (AUC 0.94). The model also performed well for prediction of 180-day mortality (AUC 0.87; mortality 74.8% in the highest risk decile vs. 0.2% in the lowest). Predictions were more accurate than data from randomized trials of individual chemotherapies, or SEER estimates.\n\nInterpretationA machine learning algorithm accurately predicted short-term mortality in patients starting chemotherapy using EHR data. Further research is necessary to determine generalizability and the feasibility of applying this algorithm in clinical settings.

bioinformatics↗

bioRxiv2017

Simulations for Designing and Interpreting Intervention Trials in Infectious Diseases

Here we urge the adoption of a new paradigm for the design and interpretation of intervention trials in infectious diseases, particularly in emerging infectious disease, that more accurately reflects the dynamics of the transmission process. Interventions in infectious diseases can have indirect effects on those not receiving the intervention as well as direct effects on those receiving the intervention. Combinations of interventions can have complex interactions at the population level. These often cannot be adequately addressed with standard study designs and analytic methods. Simulations can help to accurately represent transmission dynamics in an increasingly complex world which is critical for proper trial design and interpretation. Some ethical aspects of a trial can also be quantified using simulations. After a trial has been conducted, simulations can be used to explore possible explanations for the observed effects. A great deal is to be gained through a multidisciplinary approach that builds collaborations among experts in infectious disease dynamics, epidemiology, statistical science, economics, simulation methods and the conduct of clinical trials.

epidemiology↗

bioRxiv2026

TigerAI: An AI-powered genetic evidence platform to support clinical development

Genetic evidence is a major determinant of clinical success in drug development, yet its aggregation has long relied on laborious human curation. Large language models (LLMs) have the potential to rapidly synthesize knowledge across biomedical resources, providing a route to scalable AI-driven genetic evidence generation. Here we develop a novel domain-grounded instruction framework to systematically evaluate GPT-5 for producing genetic evidence relevant to clinical trial success. Using 13,022 target-indication pairs from a comprehensive drug development database, we benchmark LLM-derived evidence against a recent exhaustive human expert-curated study. We find that GPT-5 yields genetic evidence that is at least as informative as expert curation for inferring clinical success, while substantially expanding coverage relative to traditional curation resources. Building on these results, we introduce TigerAI (https://tigerai.bio/), a dual-purpose platform for AI-powered genetic evidence that (i) benchmarks emerging state-of-the-art LLMs and (ii) provides an accessible service for querying reliable AI-generated genetic evidence. These contributions outline a practical, domain-grounded pathway for integrating AI-powered genetic evidence into drug development pipelines and for realizing the potential of LLMs to inform clinical success.

genetics↗

bioRxiv2017

Shortwave Infrared Fluorescence Imaging with the Clinically Approved Near-Infrared Dye Indocyanine Green

Fluorescence imaging is a method of real-time molecular tracking in vivo that has enabled many clinical technologies. Imaging in the shortwave infrared region (SWIR, 1-2 m) promises higher contrast, sensitivity, and penetration depths compared to conventional visible and near-infrared (NIR) fluorescence imaging. However, adoption of SWIR imaging in clinical settings has been limited, due in part to the absence of FDA-approved fluorophores with peak emission in the SWIR. Here, we show that commercially available NIR dyes, including the FDA-approved contrast agent indocyanine green (ICG), exhibit optical properties suitable for in vivo SWIR fluorescence imaging. Despite the fact that their emission reaches a maximum in the NIR, these dyes can be imaged non-invasively in vivo in the SWIR spectral region, even beyond 1500 nm. We demonstrate real-time fluorescence angiography at wavelengths beyond 1300 nm using ICG at clinically relevant doses. Furthermore, we show tumortargeted SWIR imaging with trastuzumab labeled with IRDye 800CW, a NIR dye currently being tested in multiple phase II clinical trials. Our findings suggest that high-contrast SWIR fluorescence imaging can be implemented alongside existing imaging modalities by switching the detection of conventional NIR fluorescence systems from silicon-based NIR cameras to emerging indium gallium arsenide (InGaAs) SWIR cameras. Using ICG in particular opens the possibility of translating SWIR fluorescence imaging to human clinical applications.

bioengineering↗