A recent study was reported about novel pharmacologic therapies for pancreatic ductal adenocarcinoma (PDAC), including olaparib, a Poly ADP-Ribose Polymerase (PARP) inhibitor for treating breast cancer gene (BRCA)-mutated cancers, whose prevalence of these genetic abnormalities in documented cases of pancreatic cancer varies from 4% to 7%[1].
In 2019, the Food and Drug Administration granted approval for olaparib to be used to treat this disease. In the same year, a study conducted by Talia et al[1] showed that the drug significantly extended the amount of time patients lived without their disease progressing. In contrast, its toxicological profile remains underexplored, especially in cancer patients who are already dealing with the side effects of chemotherapy.
We conducted a comprehensive in silico analysis to evaluate the structural, physicochemical, and toxicological properties of olaparib, a PARP inhibitor used in the treatment of BRCA-mutated PDAC[2–4]. The chemical structure of olaparib was retrieved from databases including ChemBL, PubChem, DrugBank, and ChemSpider, which provided essential molecular information such as molecular weight, hydrogen bond donors and acceptors, topological polar surface area, and logP. These parameters are critical for understanding the drug’s pharmacokinetic profile and its interaction with biological systems.
To predict the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of olaparib, we utilized computational tools such as PreADMET (https://preadmet.webservice.bmdrc.org/), FAFDrugs4 (https://mobyle.rpbs.univ-paris-diderot.fr/cgi-bin/portal.py?form=FAF-Drugs4#forms::FAF-Drugs4), ADMETSAR (http://lmmd.ecust.edu.cn/admetsar2), MolInspiration (https://www.molinspiration.com/cgi-bin/properties), pkCSM (https://biosig.lab.uq.edu.au/pkcsm/prediction), SwissADME (http://www.swissadme.ch/) and ADMET-AI (https://admet.ai.greenstonebio.com/). These tools were employed to forecast various pharmacokinetic properties and potential toxicity endpoints, including hepatotoxicity, nephrotoxicity, genotoxicity, and environmental impact. The data from these predictions were then analyzed to identify theoretical chemical and medical issues associated with olaparib, providing a detailed theoretical profile that highlights areas where the drug may pose risks or challenges in clinical use.
According to our in silico predictions, several structural, physicochemical, and toxicological parameters of olaparib suggest theoretical chemical medical problems, including the potential to induce hepatotoxicity, nephrotoxicity, genotoxicity, and other toxicological endpoints as shown in Figure 1. Some studies underscore the importance of in silico predictions in assessing potential toxic effects of drugs[5,6].