Using Computational Platforms to Accelerate Drug Development
How can VeriSIM get novel therapies to COVID-19 patients faster?
— — — — — — — — — — — — — — — — — — — — — — — — — —
With the recent COVID-19 pandemic, we are especially aware of the sense of urgency in the healthcare space around discovering new treatments, and want to do our part to drive the work forward. VeriSIM utilizes computational methods which simulate human & animal physiological phenomena that quickly identify & prioritize the solutions that have the highest probability of preclinical & clinical success. This will enable “fast tracking” of the best candidates and save precious testing cycles (and time) in the process.
— — — — — — — — — — — — — — — — — — — — — — — — — —
Pharmaceutical Drug Development Process
Getting a new drug into market requires testing and running an array of experimental & animal experiments necessary for getting an assessment of drug safety/efficacy that can be slow, time-consuming, and incredibly expensive. Additionally, animal experiments (preclinical) may not represent the predictors of human outcomes (clinical) in the majority of cases which can lead to further slow down or restart of the drug finding process!
Such trial and error methods are extremely costly for pharmaceutical companies looking to develop novel compounds, can handicap researchers who can’t fund testing of their own promising therapies. There have been previous generations of computational tools developed in an attempt to accelerate development and solve the ever-present issue of tying preclinical findings to meaningful clinical outcomes. However, they are hard to use, don’t integrate with machine learning approaches, and are based on small datasets.
Additionally, the COVID-19 pandemic has exacerbated the weaknesses with the current paradigm. Many animal facilities and laboratories both across the United States and globally have either closed down or been repurposed, effectively bottlenecking therapeutic development. During the current lockdowns methodologies that decrease hands-on, in-person laboratory experimentation are required to generate therapeutics targeting the SARS-CoV-2 virus and associated conditions, and to maintain development pipelines for other compounds.
How VeriSIM Can Help
Computational modeling is the perfect avenue for maintaining scientific productivity in these times. After data is properly labeled, it can be integrated with computational advances to gain statistical insight or to scale the outputs from basic experiments to comprehensive in vivo prediction.
Our work at VeriSIM enables exactly this type of computational insight; we’ve empowered research in both, small biotech organizations and the top large pharmaceutical companies. We integrate mechanistic models of physiological mechanisms with machine learning predictions of parameters trained on known outcomes to generate predictions of drug disposition in humans and animals. Our platform encompasses different classes of therapeutics and disease conditions, including data from sources such as in silico structural computation, in vitro assay (experimental) measurements, and in vivo (animal) compound and biomarker levels.
Our technology, called “BIOiSIM”, can identify and prioritize the solutions that have the highest probability of preclinical or clinical success. This approach enables “fast tracking” of the best candidates and reduces testing cycles (and time) in the process.
VeriSIM’s platform also allows researchers to apply the power of machine learning to pharmaceutical development without requiring them to be software specialists. Scientists can integrate their own knowledge/database with VSL’s physiologically relevant models and AI platform, and train models on users’ own data while using our proprietary hybrid cloud to maintain confidence in data security. Overall, our product streamlines the development pipeline for pharma companies by enabling them to prioritize compounds that have a greater chance of success in humans; this can reduce net amount of human and animal experimentation and consequently, cut costs for pharma companies by greater than 90%!
How we accelerate research
If you have potential drug candidates for the treatment of patients with COVID-19 including, but not limited to, antivirals (agents that decrease viral load), agents that prevent viral spread (e.g. prevention of binding to ACE-2), treatments for pneumonia, prevention of cytokine storm, potential for vaccination, etc., please reach out to us!
In return, VeriSIM will run your compound against any existing internal models within 72 hours and/or develop custom models to:
- Make recommendations on proposed human pharmacology, including first-in-human dosing, route of administration, and formulation
- Propose population stratification based on known biomarkers to better design human trials for faster results (smaller sample sizes, rapid patient recruitment, shorter time to outcomes)
- Translate results from in vitro systems (organoids, microsomes, organ-on-chip, or any novel ones!) to in vivo
- For multiple candidates, assign probability of clinical success to allow for prioritization
- Model custom therapy disposition (e.g. conjugate molecules, specific formulation)
For more experienced users such as modelers, developers, and computational scientists, API calls can be provided to enable individualized model building and testing. We are also willing to provide discounted prices to accelerate the efforts! Please reach out at info@verisimlife.com to learn more.
About VeriSIM
VeriSIM Life was founded by Dr. Jo Varshney, DVM, PhD and with an exceptional cross-functional team of engineers, modelers and scientists has built a validated platform that delivers accurate predictions of in vivo compound disposition for humans and preclinical species. Collaborations with large pharmaceutical companies, small biotech companies, and alliances with academic partners have yielded outstanding results and contract renewals. Both, the positive feedback we have received from labs that we’ve interviewed and continued engagement with these partnerships have validated the team’s domain expertise and the approach it’s taking.
