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We’ve already explored how AI can aid in discovering patterns and uncovering relationships, leading us to naturally infer that a highly trained AI model could leverage prior knowledge of molecular structures to identify novel medications and accelerate treatments for some of the most challenging diseases and medical conditions.
One firm, based mainly in San Francisco, aims to revolutionize pharmaceutical research through its platform, BIOiSIM, which boasts an extensive knowledge lake comprising over 3 million compounds and 5,000 human and animal datasets, all powered by AI models trained on this rich trove of data. This allows researchers to identify, develop, and test new compounds nearly in advance of investing in actual clinical trials.
Last year, VeriSIM introduced a novel feature to its platform, focused on providing organic translation simulations – predictions of a drug’s actual efficacy in the human or animal body. Pharmaceutical researchers can leverage this information to determine not only which novel medications are experiencing price increases in real-time but also which species of animals to test them on effectively.
“We’re pleased to announce that we’ve made significant strides in reducing our reliance on animal testing,” said VeriSIM Life CEO and founder Dr. In an interview with VentureBeat several months ago, Jo Varshney spoke publicly. We can actually optimize these animal studies to eliminate the requirement for large cohorts of 50 animals or more. One might simply reexamine their medication data numerically and verify it more rigorously using computational methods.
Furthermore, this approach not only proves more compassionate towards animals but also yields cost-effective results for researchers, as they can avoid conducting tests on species whose biological makeup precludes a meaningful evaluation of the medication’s effectiveness.
For Varshney, a personal quest unfolds: her father’s involvement in the pharmaceutical industry is merely one aspect, as she has been fascinated by the field since childhood. According to VentureBeat, her interest was piqued from the tender age of two, and she began her career as a veterinarian before pursuing a PhD in genomics and computational sciences at the University of California, San Francisco.
“Prior to embarking on the corporate venture, I dedicated considerable time to machine learning, specifically exploring supervised and unsupervised approaches, and posing the question: ‘Given our existing understanding of medicine, biology, and chemistry, can we leverage this knowledge to make predictions for novel data, novel chemistries, or novel molecules?'”
So far, VeriSIM Life has successfully supported four customers in conducting clinical trials with their medication, as cited by Varshney.
The corporation has received investments from a diverse group of renowned venture capital firms, including Intel Capital, Village Global, SOSV (Susa Ventures), Stage One Ventures, Loup Ventures, and Twin Capital Partners.
The major challenge with current drug analysis and discovery: it is an expensive process with an unacceptably high failure rate.
The global pharmaceutical industry valued around $1.6 trillion, according to the latest market data from a leading research firm. However, a staggering 10-fold increase in R&D spending for new medications has been observed in the United States. The adjusted value of $1 alone in Nineteen Eighties, factoring in inflation, was based on the.
Despite a staggering 90% failure rate in medication testing, as reported by , the typical drug requires and undergoes a lengthy process taking approximately !
According to VeriSIM, its BIOiSIM platform is expected to streamline the process of transitioning from data analysis and growth to securing FDA approval for clinical trials, potentially shrinking the timeframe by approximately 2.5 years.
The AI-powered platform allegedly boasts a remarkable 82% increase in precision when simulating medication outcomes compared to traditional, non-AI approaches.
Beneath the surface of VeriSIM Life’s innovative offerings lies a complex interplay of cutting-edge technologies. The BIOiSIM platform leverages sophisticated computational methods, such as machine learning algorithms and advanced statistical models, to generate accurate and realistic digital representations of biological systems. This enables researchers to simulate complex biological processes in silico, reducing the need for costly and time-consuming wet-lab experiments.
VeriSIM Life’s BIOiSIM platform leverages cutting-edge technology, incorporating multiple AI models and datasets to drive innovation, as posited by Varshney.
“We leverage AI-powered machine learning strategies, including generative adversarial networks (GANs) and generative AI, to efficiently generate new molecules across an astonishingly vast area of 10^63 possibilities, subsequently distilling them down to the optimal molecular structure,” the CEO and founder told VentureBeat.
By leveraging cutting-edge technology, VeriSIM Life has developed a suite of digital replicas, including human subjects, as well as commonly used animal models such as dogs, rats, and pigs, that are regularly employed in pharmaceutical testing to evaluate the efficacy of novel drug candidates.
We integrated data from chemistry, physiology, and various animal models employed in testing, subsequently codifying this information, followed by consolidating patient-based ‘omics knowledge – including genomics, proteomics, and other relevant domains – to derive a comprehensive rating, known as the Translational Index. Impressed by one’s credit score rating.
The rating, a ranking between one and ten, with ten signifying the most effective and one indicating the least, allows pharmaceutical researchers to swiftly determine whether a drug warrants further investigation through clinical trials, while also crucially – selecting which animal models to use to achieve the desired outcomes.
Researchers seeking to validate a novel LDL cholesterol lowering medication might leverage VeriSIM Life’s BiOSIM AtlasGEN tool to identify optimal compounds, followed by the Translational Index to determine scores for the most suitable animal models and predict human performance, thereby providing a streamlined approach to focus their efforts for success?
“When efficacy is demonstrated in animal studies, but fails to translate to humans, the rating typically takes a hit,” said Varshney to VentureBeat.
According to VeriSIM’s website, BIOiSIM and AtlasGEN can simulate more than 800 billion unique scenarios collectively.
Varshney explained to VentureBeat that a team of laptop engineers and certified researchers continually updates the platform and designs animals to suit individual customer needs, tailoring the experience according to specific requirements.
“For instance, when we understand that a drug can be toxic within the liver, our specialists create detailed models of the liver to demonstrate how toxicity would vary between rats, dogs, and humans – this kind of nuance requires significant attention from our team.”
As for monetization, VeriSIM life reportedly takes a percentage of drug income generated on its platform, while also offering a subscription-based software-as-a-service pricing model, either annually or per project basis.
As part of a burgeoning wave of AI-powered healthcare applications and platforms.
AI’s increasing presence within the healthcare sector extends far beyond just drug discovery.
We have also implemented a system that provides personalized cancer screening recommendations to medical professionals based on patient profiles, suggesting tailored approaches for each individual. This comprehensive tool enables healthcare providers to offer precise guidance, streamlining the diagnostic process and improving treatment outcomes.
As VeriSIM strives to leave an indelible mark with its pioneering BIOiSIM platform and innovative Translational Index scores, it aims to revolutionize the pharmaceutical landscape by driving down costs, boosting trial success rates, and ultimately extending and enhancing human life globally.
