Idiosyncratic adverse drug reactions are, by definition, rare and occur unpredictably. More common drug reactions (side effects) are often caught in the trial phase, however, idiosyncratic adverse drug reactions sometimes aren’t discovered until released into the market. In 2018, a deep learning algorithm was reported that successfully predicts when furan-containing pharmaceutical candidates are likely to be metabolically oxidized, forming cytotoxic compounds. Unfortunately, due to the nature of deep learning, rationalization on <i>why</i> some compounds exhibit idiosyncratic toxicity (and others don’t) does not follow. Herein, we present analysis of the frontier molecular orbitals and the calculated heat of reaction for epoxidation of each of these potential therapeutics (the oxidation that leads to toxic compounds in the human body). The heats of reaction appear to correlate to the index of toxicity determined by deep learning. Efforts to further explore this behavior making use of linear free energy relationships will also be presented. Overall, these data may help describe why some of the molecules are prone to epoxidation and whether or not that cytotoxic pathway can be frustrated.

Frustrating idiosyncratic cytotoxicity with computational chemistry

ACS Spring Meeting 2021

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