White Papers, Case Studies, eBooks & Toolkits

.jpeg)

NIH Human-Based Research Toolkit: Unlocking Predictive Power, Reducing Costs, and Accelerating Translation
08/14/2015
The NIH’s groundbreaking shift to prioritize human-relevant science marks a paradigm change for biomedical research institutions. This toolkit outlines how VeriSIM Life’s BIOiSIM® platform enables researchers to align with NIH’s new direction, replacing traditional animal testing with predictive, clinically relevant AI-driven tools. For institutions, this shift doesn’t just improve science—it enhances funding competitiveness, reduces R&D costs, and accelerates translational impact.
Read More

Complex Organ Toxicity: Hybrid AI Provides Superior Prediction Accuracy with Very Limited Data
Drug-induced liver injury (DILI) is a major concern in the pharmaceutical industry, accounting for a significant number of drug failures and withdrawals. Traditional ways to predict DILI rely a lot on animal studies and in vitro tests. These can take a long time, cost a lot, and often don't accurately predict human DILI. As a result, there is a growing interest in using computational approaches to predict DILI. However, these methods fail to adequately consider detailed aspects of interspecies differences in drug pharmacological behavior, gene dysregulation due to the drugs, accurate drug chemistry, and integration of specific liver toxicity pathways. VeriSIM Life performed research aimed to solve these problems by leveraging knowledge-AI hybrid technology (hybrid AI) to provide robust solutions even in severely data-limited scenarios.
Read More

.jpg)

AI-Generated Virtual Patients Enhance Clinical Trial Outcomes: a Diabetes Treatment Analysis
VeriSIM Life utilized its hybrid AI platform, BIOiSIM, to address the challenges of limited and missing data from clinical trials. The approach included imputing missing biomarker data utilizing hybrid AI models to reduce missing values with high robustness and enriching patient populations, including underrepresented groups, through AI-driven virtual patient generation (with 10,000 virtual patients generated). The use of such enriched data also allowed for the use of Explainable AI to identify the most important biomarkers - enabling accurate patient stratification.
Read More

AtlasGen Novel Drug Designer: Design Clinical Success Into New Drug Discovery from Day One
05/30/2024
AtlasGEN efficiently identifies new molecules based on favorable chemical properties and simultaneously evaluates them for clinical safety and effectiveness based on biology using VeriSIM Life’s groundbreaking Translational Index™ technology. With AtlasGEN, pharmaceutical companies can take advantage of AI-driven drug design chemistry with biological validation to discover clinically successful novel therapies at the beginning of the drug design process. It reduces the time and cost associated with other computational hit discovery approaches, while compressing the hit-to-lead refinement and optimization process. Download the white paper to learn more.
Read More

Preempting a Clinical Dead End with Metabolite Analysis Powered by Hybrid AI
Optimizing a drug candidate, even when demonstrating strong efficacy, can be a challenge when metabolites may be involved in creating the intended therapeutic effect. VeriSIM Life’s client needed to confirm their compound’s efficacy as direct or indirect and determine their best path forward for developing a novel cancer therapy. BIOiSIM confirmed a metabolite-driven mechanism of action, identified a range of efficacious metabolites, and ranked them based on both potency and safety.
Read More

Predicting Translatable Drug Combinations for ADCs - A Pharma Partner Case Study
01/18/2024
VeriSIM Life investigated for our client Debiopharm a novel drug combination strategy that combines ADCs with current therapies to enhance their effectiveness in reducing tumor burden. Our hybrid AI computational platform, BIOiSIM®, ranked the predicted optimal combinations with regard to efficacy based on our multi-dimensional Translational Index™ technology.
Read More

How to Evolve from Traditional Model-Informed Drug Discovery & Development to an AI-Informed Approach
7/25/2023
The purpose of this eBook is to demonstrate that an integrated MIDD+AI approach can assist with preclinical operations while minimizing disruption. In fact, experts currently using traditional R&D methods already have extensive knowledge and experience necessary to support the multidisciplinary harmonization of data needed to leverage integrated AI-enhanced MIDD frameworks and workflows.
Read More

%20(2).png)

Accelerating SUD Therapy Discovery with AI/ML Driven BBB Permeability Predictions
4/3/2023
VeriSIM Life partnered with our client to translate drug candidates targeting SUD patient therapies more rapidly and accurately than traditional methods that depend on the experimental uncertainty of modeling for data.
Read More

Pathways for Successful AI Adoption in Drug Development
Artificial intelligence in drug development has transformative potential when it comes to pharma research and development (R&D). Download the white paper to advance the adoption of AI within your organization.
Read More

Frost & Sullivan Report: AI-enabled Preclinical Development, VeriSIM Life
12/13/2022
Frost & Sullivan 2022 Competitive Strategy Leader Award North American Artificial Intelligence Enabled Preclinical Development Industry
Read More

Predicting Patient-Specific Drug Bioavailability with AI
09.28.2020
This study covers one of the many applications of BIOiSIM. Our aim was to establish what the pharmacokinetic (PK) profile would look like in the different subjects studied.
Read More

BIOiSIM™ Drug Decision Engine: Breakthrough Intelligence to De-risk R&D Translation, Without Disruption
09.30.2022
This white paper showcases how fusing artificial intelligence and mechanistic modeling can be used to discover novel drugs and accurately predict the disposition of small molecules across different subjects for translation to clinical success.
Read More

Optimizing Dosing with AI/ML Driven Transdermal Drug Permeation Simulation
07.22.2020
This study covers the development and validation of a transdermal model using the BIOiSIM framework, which was further used to describe the relationship between compound exposure and therapeutic effect.
Read More