August 2020CIOAPPLICATIONS.COM 19variety of diseases and infections such as cancer, diabetes, and, more recently, COVID-19.The novel computational framework for designing rule-based simulations of in vivo phenomena includes a database architecture that integrates readily with curated data, tracks subject-specific physiological differences, and enables training and evaluation of model performance using experimental data points across diverse validation datasets. This approach enables the development of predictive models of disease without overfitting specific datasets. The observed predictions are translatable across different species and routes of administration and applied to large datasets of 1000+ of compounds without sacrificing accuracy.BIOiSIM includes a robust model for predicting both small and large molecules in vivo pharmacokinetics, as well as for healthy and patient biomarker pathways. Because the model has been developed and assessed against known outcomes, the complexity of the underlying core model equations is balanced with the ability to translate to new experimental conditions by a critical analysis of model performance. The platform has been applied successfully to the optimization of transdermal dosing for combination therapies, modeling of diabetes signaling pathways, and is currently being expanded from modalities of small molecules to large molecules, CRISPR-Cas, and viral systems and drug combinations against COVID-19.How does VeriSIM's platform enable faster model development, more accurate prediction, and higher scalability? Please elaborate.BIOiSIM is a dynamic, biology-driven platform that provides a scalable computational prediction of in vivo PK/PD phenomena. A whole-body 16-compartment model of systems biology and PK/PD is integrated with AI/ML to make accurate and computationally scalable predictions that are applicable to compound datasets with gaps and variability. The integration of ML with mechanistic modeling allows the platform to fill-in missing data gaps through optimization/prediction given datasets of various completeness.We have invested heavily in our database and simulation capabilities. VSL has developed the largest proprietary curated database consisting of structure-related data for >1M compounds, >3700+ unique in vivo plasma concentration-time validation datasets from public and proprietary sources representing ~2000 unique compounds and 83 different subject populations (different species, gender, strain, sub-strain). Further, our software systems provide the simulation time for a drug-species PK/PD experimental setup in the order of seconds; this scalability escalates computational capabilities beyond the current paradigm of on-premise pharmacological software.BIOiSIM can predict PK-specific drug toxicities based on various drug dosages and methods of the drug's administration (oral, IV, intranasal, or transdermal) in healthy human/animal subjects with over 81 percent accuracy, validated by existing preclinical and clinical data.By integrating machine learning algorithm-based approaches into traditional physiological modeling and simulation, we can build more complicated physiologically-based models informed by different cell types, pathways, and signaling components, to ultimately reduce drug failure rates in clinical trials and minimize the need for animal testing. We also use a multi-omics approach that integrates how personalized, patient-specific, drug-specific, and disease-specific parameters affect how the body responds to a drug and how a drug responds to the body. The result is that VSL can provide more precise data to facilitate decision making during drug development.What sets VSL apart from its competitors?Currently, there are more than 250 companies active that specifically implement AI/ML techniques across the drug life cycle. Most of these companies operate in the early drug discovery phase (high throughput target screening) instead of the late discovery phase and early drug development where VSL services are being provided. Moreover, we are complementary to several of these AI/ML companies in the drug discovery space.Our direct competitors are mechanistic modeling companies, but our customer-validated value propositions and unique ML/AI integrations scale beyond their offerings into formulation strategy, experimental drug designs, and patient stratifications. This gives us an additional competitive edge in the market. Further, we have established unique multi-way partnerships with industry and academia to develop joint-IPs in addition to providing software subscription, which not only expands our assets but also leads in capturing the market even further. The holistic integration of various datasets makes us the first-in-business to enable accurate insight and translatability into personalized medicine by integrating species/patient-specific physiological variabilities and disease states into the platform and validating the prediction accuracy across different classes of therapeutics.What does the future hold for VSL?Our mission is to help design better drugs, predict disease outcomes, and select specific patient populations who would benefit from therapies, making the dream of personalized medicine a reality. For this, we will grow a team to include world-class talent in the engineering and operations space and keep optimizing our modeling and simulation practices through expanded partnerships. As we expand, we will maintain a cross-collaborative culture reliant upon world-class subject matter expertise and an approach to collaboration that seeks to include as many diverse experiences as possible. We use world-class expertise in machine learning, mechanistic modeling, and engineering to produce models that serve as `digital' representations of animals, including humans
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