Leveraging Big Data to Transform Drug Discovery

Foundational model aided automatic high-throughput drug screening using self-controlled cohort study

Background

Developing medicine from scratch to governmental authorization and detecting adverse drug reactions (ADR) have barely been economical, expeditious, and risk-averse investments. The availability of large-scale observational healthcare databases and the popularity of large language models offer an unparalleled opportunity to enable automatic high-throughput drug screening for both repurposing and pharmacovigilance.

Objectives

To demonstrate a general workflow for automatic high-throughput drug screening with the following advantages: (i) the association of various exposure on diseases can be estimated; (ii) both repurposing and pharmacovigilance are integrated; (iii) accurate exposure length for each prescription is parsed from clinical texts; (iv) intrinsic relationship between drugs and diseases are removed jointly by bioinformatic mapping and large language model - ChatGPT; (v) causal-wise interpretations for incidence rate contrasts are provided.

Methods

Using a self-controlled cohort study design where subjects serve as their own control group, we tested the intention-to-treat association between medications on the incidence of diseases. Exposure length for each prescription is determined by parsing common dosages in English free text into a structured format. Exposure period starts from initial prescription to treatment discontinuation. A same exposure length preceding initial treatment is the control period. Clinical outcomes and categories are identified using existing phenotyping algorithms. Incident rate ratios (IRR) are tested using uniformly most powerful (UMP) unbiased tests.

Results

We assessed 3,444 medications on 276 diseases on 6,613,198 patients from the Clinical Practice Research Datalink (CPRD), an UK primary care electronic health records (EHR) spanning from 1987 to 2018. Due to the built-in selection bias of self-controlled cohort studies, ingredients-disease pairs confounded by deterministic medical relationships are removed by existing map from RxNorm and nonexistent maps by calling ChatGPT. A total of 16,901 drug-disease pairs reveals significant risk reduction, which can be considered as candidates for repurposing, while a total of 11,089 pairs showed significant risk increase, where drug safety might be of a concern instead.

Conclusions

This work developed a data-driven, nonparametric, hypothesis generating, and automatic high-throughput workflow, which reveals the potential of natural language processing in pharmacoepidemiology. We demonstrate the paradigm to a large observational health dataset to help discover potential novel therapies and adverse drug effects. The framework of this study can be extended to other observational medical databases.

Computational drug discovery pipelines identify NAMPT as a therapeutic target in neuroendocrine prostate cancer

Neuroendocrine prostate cancer (NEPC) is an aggressive advanced subtype of prostate cancer that exhibits poor prognosis and broad resistance to therapies. Currently, few treatment options are available, highlighting a need for new therapeutics to help curb the high mortality rates of this disease. We designed a comprehensive drug discovery pipeline that quickly generates drug candidates ready to be tested. Our method estimated patient response to various therapeutics in three independent prostate cancer patient cohorts and selected robust candidate drugs showing high predicted potency in NEPC tumors. Using this pipeline, we nominated NAMPT as a molecular target to effectively treat NEPC tumors. Our in vitro experiments validated the efficacy of NAMPT inhibitors in NEPC cells. Compared with adenocarcinoma LNCaP cells, NAMPT inhibitors induced significantly higher growth inhibition in the NEPC cell line model NCI-H660. Moreover, to further assist clinical development, we implemented a causal feature selection method to detect biomarkers indicative of sensitivity to NAMPT inhibitors. Gene expression modifications of selected biomarkers resulted in changes in sensitivity to NAMPT inhibitors consistent with expectations in NEPC cells. Validation of these markers in an independent prostate cancer patient dataset supported their use to inform clinical efficacy. Our findings pave the way for new treatments to combat pervasive drug resistance and reduce mortality. Furthermore, this research highlights the use of drug sensitivity-related biomarkers to understand mechanisms and potentially indicate clinical efficacy.

Accelerating drug discovery and repurposing by combining transcriptional signature connectivity with docking

We present an in silico approach for drug discovery, dubbed connectivity enhanced structure activity relationship (ceSAR). Building on the landmark LINCS library of transcriptional signatures of drug-like molecules and gene knockdowns, ceSAR combines cheminformatic techniques with signature concordance analysis to connect small molecules and their targets and further assess their biophysical compatibility using molecular docking. Candidate compounds are first ranked in a target structure-independent manner, using chemical similarity to LINCS analogs that exhibit transcriptomic concordance with a target gene knockdown. Top candidates are subsequently rescored using docking simulations and machine learning-based consensus of the two approaches. Using extensive benchmarking, we show that ceSAR greatly reduces false-positive rates, while cutting run times by multiple orders of magnitude and further democratizing drug discovery pipelines. We further demonstrate the utility of ceSAR by identifying and experimentally validating inhibitors of BCL2A1, an important antiapoptotic target in melanoma and preterm birth-associated inflammation.

Incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease in patients with multiple sclerosis initiating disease-modifying therapies: Retrospective cohort study using a frequentist model averaging statistical framework

Researchers are increasingly using insights derived from large-scale, electronic healthcare data to inform drug development and provide human validation of novel treatment pathways and aid in drug repurposing/repositioning. The objective of this study was to determine whether treatment of patients with multiple sclerosis with dimethyl fumarate, an activator of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, results in a change in incidence of type 2 diabetes and its complications. This retrospective cohort study used administrative claims data to derive four cohorts of adults with multiple sclerosis initiating dimethyl fumarate, teriflunomide, glatiramer acetate or fingolimod between January 2013 and December 2018. A causal inference frequentist model averaging framework based on machine learning was used to compare the time to first occurrence of a composite endpoint of type 2 diabetes, cardiovascular disease or chronic kidney disease, as well as each individual outcome, across the four treatment cohorts. There was a statistically significantly lower risk of incidence for dimethyl fumarate versus teriflunomide for the composite endpoint (restricted hazard ratio [95% confidence interval] 0.70 [0.55, 0.90]) and type 2 diabetes (0.65 [0.49, 0.98]), myocardial infarction (0.59 [0.35, 0.97]) and chronic kidney disease (0.52 [0.28, 0.86]). No differences for other individual outcomes or for dimethyl fumarate versus the other two cohorts were observed. This study effectively demonstrated the use of an innovative statistical methodology to test a clinical hypothesis using real-world data to perform early target validation for drug discovery. Although there was a trend among patients treated with dimethyl fumarate towards a decreased incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease relative to other disease-modifying therapies-which was statistically significant for the comparison with teriflunomide-this study did not definitively support the hypothesis that Nrf2 activation provided additional metabolic disease benefit in patients with multiple sclerosis.

Development of digital health management systems in longitudinal study: The Malaysian cohort experience

Background

The management of extensive longitudinal data in cohort studies presents significant challenges, particularly in middle-income countries like Malaysia where technological resources may be limited. These challenges include ensuring data integrity, security, and scalability of storage solutions over extended periods.

Objective

This article outlines innovative methods developed and implemented by The Malaysian Cohort project to effectively manage and maintain large-scale databases from project inception through the follow-up phase, ensuring robust data privacy and security.

Methods

We describe the comprehensive strategies employed to develop and sustain the database infrastructure necessary for handling large volumes of data collected during the study. This includes the integration of advanced information management systems and adherence to stringent data security protocols.

Outcomes

Key achievements include the establishment of a scalable database architecture and an effective data privacy framework that together support the dynamic requirements of longitudinal healthcare research. The solutions implemented serve as a model for similar cohort studies in resource-limited settings. The article also explores the broader implications of these methodologies for public health and personalized medicine, addressing both the challenges posed by big data in healthcare and the opportunities it offers for enhancing disease prevention and management strategies.

Conclusion

By sharing these insights, we aim to contribute to the global discourse on improving data management practices in cohort studies and to assist other researchers in overcoming the complexities associated with longitudinal health data.

Drug repositioning targeting glutaminase reveals drug candidates for the treatment of Alzheimer's disease patients

BACKGROUND

Despite numerous clinical trials and decades of endeavour, there is still no effective cure for Alzheimer's disease. Computational drug repositioning approaches may be employed for the development of new treatment strategies for Alzheimer's patients since an extensive amount of omics data has been generated during pre-clinical and clinical studies. However, targeting the most critical pathophysiological mechanisms and determining drugs with proper pharmacodynamics and good efficacy are equally crucial in drug repurposing and often imbalanced in Alzheimer's studies.

METHODS

Here, we investigated central co-expressed genes upregulated in Alzheimer's disease to determine a proper therapeutic target. We backed our reasoning by checking the target gene's estimated non-essentiality for survival in multiple human tissues. We screened transcriptome profiles of various human cell lines perturbed by drug induction (for 6798 compounds) and gene knockout using data available in the Connectivity Map database. Then, we applied a profile-based drug repositioning approach to discover drugs targeting the target gene based on the correlations between these transcriptome profiles. We evaluated the bioavailability, functional enrichment profiles and drug-protein interactions of these repurposed agents and evidenced their cellular viability and efficacy in glial cell culture by experimental assays and Western blotting. Finally, we evaluated their pharmacokinetics to anticipate to which degree their efficacy can be improved.

RESULTS

We identified glutaminase as a promising drug target. Glutaminase overexpression may fuel the glutamate excitotoxicity in neurons, leading to mitochondrial dysfunction and other neurodegeneration hallmark processes. The computational drug repurposing revealed eight drugs: mitoxantrone, bortezomib, parbendazole, crizotinib, withaferin-a, SA-25547 and two unstudied compounds. We demonstrated that the proposed drugs could effectively suppress glutaminase and reduce glutamate production in the diseased brain through multiple neurodegeneration-associated mechanisms, including cytoskeleton and proteostasis. We also estimated the human blood-brain barrier permeability of parbendazole and SA-25547 using the SwissADME tool.

CONCLUSIONS

This study method effectively identified an Alzheimer's disease marker and compounds targeting the marker and interconnected biological processes by use of multiple computational approaches. Our results highlight the importance of synaptic glutamate signalling in Alzheimer's disease progression. We suggest repurposable drugs (like parbendazole) with well-evidenced activities that we linked to glutamate synthesis hereby and novel molecules (SA-25547) with estimated mechanisms for the treatment of Alzheimer's patients.

Drug repurposing using real-world data

The use of real-world data in drug repurposing has emerged due to well-established advantages of drug repurposing in supplementing de novo drug discovery and incentives in incorporating real-world evidence in regulatory approvals. We conducted a scoping review to characterize repurposing studies using real-world data and discuss their potential challenges and solutions. A total of 250 studies met the inclusion criteria, of which 36 were original studies on hypothesis generation, 101 on hypothesis validation, and seven on safety assessment. Key challenges that should be addressed for future progress in using real-world data for repurposing include isolated data sources with poor clinical granularity, false-positive signals from data mining, the sensitivity of hypothesis validation to bias and confounding, and the lack of clear regulatory guidance.

Overview of Bioinformatics Software and Databases for Metabolic Engineering

The explosion of the "omics" era has introduced a growing number of sets and tools that facilitate molecular interrogation of the metabolome. These include various bioinformatics and pharmacogenomics resources that can be utilized independently or collectively to facilitate metabolic engineering across disease, clinical oncology, and understanding of molecular changes across larger systems. This review provides starting points for accessing publicly available data and computational tools that support assessment of metabolic profiles and metabolic regulation, providing both a depth-and-breadth approach toward understanding the metabolome. We focus in particular on pathway databases and tools, which provide in-depth analysis of metabolic pathways, which is at the heart of metabolic engineering.

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

From traditional to data-driven medicinal chemistry: a case study

Artificial intelligence (AI) and data science are beginning to impact drug discovery. It usually takes considerable time and effort until new scientific concepts or technologies make a transition from conceptual stages to practical applicability and until experience values are gathered. Especially for computational approaches, demonstrating measurable impact on drug discovery projects is not a trivial task. A pilot study at Daiichi Sankyo Company has attempted to integrate data-driven approaches into practical medicinal chemistry and quantify the impact, as reported herein. Although the organization and focal points of early-phase drug discovery naturally vary at different pharmaceutical companies, the results of this pilot study indicate the significant potential of data-driven medicinal chemistry and suggest new models for internal training of next-generation medicinal chemists. Keywords: medicinal chemistry; drug discovery; chemoinformatics; data science; data-driven R&D.

Genome-wide Signatures of Early-Life Stress: Influence of Sex

Both history of early-life stress (ELS) and female sex are associated with increased risk for depression. The complexity of how ELS interacts with brain development and sex to impart risk for multifaceted neuropsychiatric disorders is also unlikely to be understood by examining changes in single genes. Here, we review an emerging literature on genome-wide transcriptional and epigenetic signatures of ELS and the potential moderating influence of sex. We discuss evidence both that there are latent sex differences revealed by ELS and that ELS itself produces latent transcriptomic changes revealed by adult stress. In instances where there are broad similarities in global signatures of ELS among females and males, genes that contribute to these patterns are largely distinct based on sex. As this area of investigation grows, an effort should be made to better understand the sex-specific impact of ELS within the human brain, specific contributions of chromosomal versus hormonal sex, how ELS alters the time course of normal transcriptional development, and the cell-type specificity of transcriptomic and epigenomic changes in the brain. A better understanding of how ELS interacts with sex to alter transcriptomic and epigenomic signatures in the brain will inform individualized therapeutic strategies to prevent or ameliorate depression and other psychiatric disorders in this vulnerable population.

Proteomics and Drug Repurposing in CLL towards Precision Medicine

Simple Summary

Despite continued efforts, the current status of knowledge in CLL molecular pathobiology, diagnosis, prognosis and treatment remains elusive and imprecise. Proteomics approaches combined with advanced bioinformatics and drug repurposing promise to shed light on the complex proteome heterogeneity of CLL patients and mitigate, improve, or even eliminate the knowledge stagnation. In relation to this concept, this review presents a brief overview of all the available proteomics and drug repurposing studies in CLL and suggests the way such studies can be exploited to find effective therapeutic options combined with drug repurposing strategies to adopt and accost a more “precision medicine” spectrum.

Abstract

CLL is a hematological malignancy considered as the most frequent lymphoproliferative disease in the western world. It is characterized by high molecular heterogeneity and despite the available therapeutic options, there are many patient subgroups showing the insufficient effectiveness of disease treatment. The challenge is to investigate the individual molecular characteristics and heterogeneity of these patients. Proteomics analysis is a powerful approach that monitors the constant state of flux operators of genetic information and can unravel the proteome heterogeneity and rewiring into protein pathways in CLL patients. This review essences all the available proteomics studies in CLL and suggests the way these studies can be exploited to find effective therapeutic options combined with drug repurposing approaches. Drug repurposing utilizes all the existing knowledge of the safety and efficacy of FDA-approved or investigational drugs and anticipates drug alignment to crucial CLL therapeutic targets, leading to a better disease outcome. The drug repurposing studies in CLL are also discussed in this review. The next goal involves the integration of proteomics-based drug repurposing in precision medicine, as well as the application of this procedure into clinical practice to predict the most appropriate drugs combination that could ensure therapy and the long-term survival of each CLL patient.

Exploring real-world evidence to uncover unknown drug benefits and support the discovery of new treatment targets for depressive and bipolar disorders

BACKGROUND

Major depressive and bipolar disorders are associated with impaired quality of life and high economic burden. Although progress has been made in our understanding of the underlying pathophysiology and the development of novel pharmacological treatments, a large unmet need remains for finding effective treatment options. The purpose of this study was to identify potential new mechanisms of actions or treatment targets that could inform future research and development opportunities for major depressive and bipolar disorders.

METHODS

A self-controlled cohort study was conducted to examine associations between 1933 medications and incidence of major depressive and bipolar disorders across four US insurance claims databases. Presence of incident depressive or bipolar disorders were captured for each patient prior to or after drug exposure and incident rate ratios were calculated. Medications that demonstrated ≥50% reduction in risk for both depressive and bipolar disorders within two or more databases were evaluated as potential treatment targets.

RESULTS

Eight medications met our inclusion criteria, which fell into three treatment groups: drugs used in substance use disorders; drugs that affect the cholinergic system; and drugs used for the management of cardiovascular-related conditions.

LIMITATIONS

This study was not designed to confirm a causal association nor inform current clinical practice. Instead, this research and the methods employed intended to be hypothesis generating and help uncover potential treatment pathways that could warrant further investigation.

CONCLUSIONS

Several potential drug targets that could aid further research and discovery into novel treatments for depressive and bipolar disorders were identified.

OCTAD: an open workspace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features

As the field of precision medicine progresses, treatments for patients with cancer are starting to be tailored to their molecular as well as their clinical features. The emerging cancer subtypes defined by these molecular features require that dedicated resources be used to assist the discovery of drug candidates for preclinical evaluation. Voluminous gene expression profiles of patients with cancer have been accumulated in public databases, enabling the creation of cancer-specific expression signatures. Meanwhile, large-scale gene expression profiles of cellular responses to chemical compounds have also recently became available. By matching the cancer-specific expression signature to compound-induced gene expression profiles from large drug libraries, researchers can prioritize small molecules that present high potency to reverse expression of signature genes for further experimental testing of their efficacy. This approach has proven to be an efficient and cost-effective way to identify efficacious drug candidates. However, the success of this approach requires multiscale procedures, imposing considerable challenges to many labs. To address this, we developed Open Cancer TherApeutic Discovery (OCTAD; http://octad.org ): an open workspace for virtually screening compounds targeting precise groups of patients with cancer using gene expression features. Its database includes 19,127 patient tissue samples covering more than 50 cancer types and expression profiles for 12,442 distinct compounds. The program is used to perform deep-learning-based reference tissue selection, disease gene expression signature creation, drug reversal potency scoring and in silico validation. OCTAD is available as a web portal and a standalone R package to allow experimental and computational scientists to easily navigate the tool.

Big data and artificial intelligence in future patient management. How is it all started? Where are we at now? Quo tendimus?

Background

This article is focused on the understanding of the key points and their importance and impact on the future of early disease predictive models, accurate and fast diagnosis, patient management, optimise treatment, precision medicine, and allocation of resources through the applications of Big Data (BD) and Artificial Intelligence (AI) in healthcare.

Content

BD and AI processes include learning which is the acquisition of information and rules for using the information, reasoning which is using rules to reach approximate or definite conclusions and self-correction. This can help improve the detection of diseases, rare diseases, toxicity, identifying health system barriers causing under-diagnosis. BD combined with AI, Machine Learning (ML), computing and predictive-modelling, and combinatorics are used to interrogate structured and unstructured data computationally to reveal patterns, trends, potential correlations and relationships between disparate data sources and associations.

Summary

Diagnosis-assisted systems and wearable devices will be part and parcel not only of patient management but also in the prevention and early detection of diseases. Also, Big Data will have an impact on payers, devise makers and pharmaceutical companies. BD and AI, which is the simulation of human intelligence processes, are more diverse and their application in monitoring and diagnosis will only grow bigger, wider and smarter.

Outlook

BD connectivity and AI of diagnosis-assisted systems, wearable devices and smartphones are poised to transform patient and to change the traditional methods for patient management, especially in an era where is an explosion in medical data.