Verisimlife

Patient stratification

How AI helps with patient stratification in the context of drug development and discovery.

Introduction

Determining if a new drug or therapy is safe and effective for a total patient population, as opposed to a precision medicine that works only for a select subgroup, is the goal of patient stratification – a critically important layer in the drug development process.

Patient stratification is a process by which patients are organized into different strata or blocks according to some established criteria (e.g. gender, ethnicity, medical history, biomarker combinations, socio-economic condition, employment or any other factor deemed relevant). These strata become representative of the subgroups of an actual patient population.

Patient stratification plays a central role in the clinical trial phase of drug development, giving researchers the opportunity to maximize responsiveness, eliminate bias, and otherwise manage the treatment process – ensuring every patient subgroup receives exposure or allocation to experimental treatments.

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Situation

As an essential part of clinical trial design, patient stratification can help improve patient outcomes, reduce trial failure rates, lower costs and accelerate the development of novel targets.

Still, bringing a new drug to market presents an assortment of complex challenges for researchers, including:

Data Privacy: In order to create representative patient population cohorts, researchers need to be able to quickly find, access, use and reuse large volumes of high-quality patient datasets. Because patient data is considered highly sensitive and is often protected by regulatory guidelines, access to these datasets can be limited.
Data Integrity: In addition to privacy issues, the fragmentation of medical data across EHRs and different software platforms makes collecting and using patient information quite challenging. Data integrity in the clinical environment means data is FAIR (Findable, Accessible, Interoperable, Reusable). Falling short of that high bar, many researchers may only be able to rely on their internal and external biobanks for patient stratification.
Biomarker complexity: Identifying the unique biomarkers associated with a disease state, a response to treatment, or some other relevant biological state is hugely important when monitoring patient response to therapy or selecting the most efficacious treatments for a condition. However, when biomarker combinations are more complex, it becomes difficult to extract or decipher their larger significance.

The VeriSIM Life advantage

VeriSIM Life’s groundbreaking drug decision engine, BIOiSIM®, uses AI-driven diagnostics and screening techniques to greatly enhance traditional patient stratification methods while addressing some of drug development’s most pressing pain points.

BIOiSIM® allows access, analysis and predictive use of its growing, proprietary, structure-related data lake for >3M+ compounds, >5000+ unique animal and human validation datasets, and physiological parameters of 196+ different subject populations.

✓ Data Privacy: BIOiSIM® can create patient population representations based on virtual cohorts generated by the AI’s predictive capability. This mitigates issues of both data privacy and data integrity, as researchers no longer require access to large patient databases.

✓ Data Integrity: The BIOiSIM™ framework also combines thousands of validation data sets, multi-compartmental models, and its integrated AI/ML engine, to help ensure validity, quality and integrity of data curated by VSL and stored in its main modeling and simulation database.

✓ Biomarker Complexity: VeriSIM Life’s groundbreaking Translational Index™️ technology can be integrated across multiple biomarker types, used to evaluate and predict the efficacy and side effects of novel drug candidates in specific patient subpopulations with differing genetic, biomarker and demographic profiles.

BIOiSIM, and its groundbreaking Translational Index™️ technology

Which advances only the most promising drug candidates through R&D to investigational new drug (IND) application, offers actionable insights of unprecedented value to the drug development industry.

Combining thousands of validation data sets, multi-compartmental models, and its integrated AI/ML engine, BIOiSIM® achieves superior physiological and biological relevance within three classes of therapeutics: small molecules, large molecules, and re-engineered viruses.

AtlasGEN™️ Novel Drug Designer benefits

The BIOiSIM® platform features a robust data lake foundation, integrating:

Vast chemical search space: The ability to generate target engaging compound hits depends largely on the size of the molecular search library. The AtlasGEN search space is supported by more than 1012 compounds.

Super fast discovery: AtlasGEN accurately predicts protein-lygand binding affinity by combining geometric conformer analysis with machine learning based on experimental data X% more efficiently than molecular docking-based approaches.

Pre-validated hits: Iterative ranking of target engaging hits by their Translational Index values reduces the time and cost associated with other computational hit discovery approaches, while compressing the hit-to-lead refinement and optimization process by pre-validating hits for translatability.

Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients

Challenge

One of the greatest challenges during the COVID-19 pandemic, especially in resource-strained settings, was the early identification of individual patients at higher risk for adverse outcomes. However, to do so would necessitate intelligent risk-assessment tools that could predict a patient’s disease progression and recovery and suggest best-fit therapeutics for markedly reducing disease severity.

Solution

VeriSIM Life used AI- and ML-based patient stratification modeling linking omics and clinical biomarker datasets, focusing on COVID-19 patients. The ML model not only demonstrated that clinical features were enough of an indicator of COVID-19 severity and survival, but also inferred what clinical features were most impactful, creating a useful guide for clinicians to prioritize the best-fit therapeutics for a given cohort of patients.

Methods

Clinical Data Acquisition - The goal was to summarize all available sources providing clinical and OMICs data for individual patients infected with SARS-CoV-2 and admitted to the healthcare institutions.
Data Curation - The clinical dataset consisted of patient conditions, lab test results, and clinician reports.
Bioinformatics Methodology - Gene network analysis was conducted on gene expression data.
Descriptor Analysis and Selection - VeriSIM Life collected various types of data such as patient condition, biomarkers, comorbidities, and therapy information.
Model Training and Evaluation - Two different types of ML models were deployed for this project.

Outcome

A robust AI/ML-based model was created to stratify COVID-19 patients using OMICS, and clinical biomarker datasets, enabling accurate prediction of disease severity and outcomes. The accuracy of both models was 98.1% and 99.9%, respectively. Read the full paper.

VeriSIM Life demonstrated that patient stratification models, driven by AI/ML modeling, could be used to precisely identify the manifestation of clinical biomarkers, resulting in more accurate diagnoses and treatment options in the context of personalized medicine.

Additional VeriSIM Life Case Studies & Content

How to Evolve from Traditional Model-Informed Drug Discovery & Development to an AI-Informed Approach
Read the full article
Predicting Patient-Specific Drug Bioavailability with AI
Read the full article

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