Computational toxicology aims to use rules, models and algorithms based on prior data for specific endpoints, to enable the prediction of whether a new molecule will possess similar liabilities or not. Our recent efforts have used sources like PubChem and ChEMBL to build predictive models for different toxicity-related and drug discovery endpoints. Our Phase I SBIR proposal called MegaTox will provide toxicity machine learning models developed with different algorithms for 40-50 in vitro and in vivo toxicity datasets. We propose using this technology to generate machine learning models for predicting potential compounds against either TGF-?? a target for countering chlorine induced lung inflammation? as well as the adenosine A1 receptor to identify agonists as potential anticonvulsants. In addition, we can also compile molecules that can reactivate acetylcholinesterase which would enable the potential to discover medical countermeasures to address nerve agent and pesticide poisoning. We will access multiple machine learning approaches and validate these Bayesian or other machine learning models (including Linear Logistic Regression, AdaBoost Decision Tree, Random Forest, Support Vector Machine and deep neural networks (DNN) of varying depth) with our own in-house technology for these selected targets. We will aim for ROC values greater than 0.75 and MCC and F1 scores that are acceptable (>0.3). These models will be used to virtually screen FDA approved drugs, clinical candidates, commercially available drugs or other molecules. We will select up to 50 molecules to be tested using in vitro assays alongside controls for each target. These combined efforts should in the first instance provide commercially viable treatments which will be used to experimentally validate our computational models that can be shared with the medical countermeasures scientific community. In summary, we are proposing to build and validate models for targets based on public databases, select compounds for testing, create proprietary data and use this as a starting point for further optimization of compounds if needed. Our goal is to identify at least one promising compound for each target that we then pursue and protect our IP. We will pursue additional grant funding to take these medical countermeasures through additional in vitro and in vivo preclinical studies. Ultimately, we will license our products to larger companies for development prior to clinical trials.
There is an urgent need to develop medical countermeasures (MCM) to address pulmonary agents, nerve agents and organophosphorus pesticides. Our approach leverages public and private data to build machine learning models for different targets involved in the physiological effects of the aforementioned agents. We then use these computational models to select new molecules to test in vitro. Our approach builds on our MegaTox approach focused on modeling toxicology targets to specifically focus on identifying compounds for TGF-? and Adenosine A1 as well as potential AChE reactivators. This computational approach will be validated using in vitro testing and offers several advantages to identify potential novel or repurposed molecules as MCM including speed and cost-effectiveness.
