title: Predicting myelosuppression of drugs from in silico models.
abstract: Anticancer agents targeting proliferating cell populations in tumor as well as in normal tissues can lead to a number of side effects including hematotoxicity, a common dose-limiting toxicity associated with oncology drugs. Myelosuppression, regarded as unacceptable for other therapeutic indications, is considered a clinical risk also for new targeted anticancer drugs acting specifically on tumor cells. Thus, it becomes important not only to evaluate the potential toxicity of such new therapeutics to human hematopoietic tissue during preclinical development but also to anticipate this liability in early drug discovery. This could be achieved by using in silico models to guide the design of new lead compounds and the selection of analogs with reduced myelosuppressive potential. Hence, the purpose of this study was to develop computational models able to predict the potential myelotoxicity of drugs from their chemical structure. The data set analyzed included 38 drugs. The structural diversity and the drug-like space covered by these molecules were investigated using the ChemGPS methodology. Two sets of potentially relevant descriptors for modeling myelotoxicity (i.e., 3D Volsurf+ and 2D structural and electrotopological E-states descriptors) were selected and a Principal Component Analysis was carried out on the entire set of data. The first two PCs were able to discriminate the highest from the least myelotoxic compounds with a total accuracy of 95%. Then, a quantitative PLS model was developed by correlating a selected subset of in vitro hematotoxicity data with Volsurf+ descriptors. After variable selection, the PLS analysis resulted in a one-latent-variable model with r(2) of 0.79 and q(2) of 0.72. The inclusion of 2D descriptors in the PLS analysis improved only slightly the robustness and quality of the model that predicted the pIC(50) values of 21 drugs not included in the model with a RMSEP of 0.67 and a squared correlation coefficient (r(0)(2)) of 0.70. Furthermore, in order to investigate whether the highly myelotoxic compounds are characterized by common structural features, which should be taken into consideration in the design of new candidate drugs, the entire data set was analyzed using GRIND toxicophore-based descriptors. One toxicophore emerged from the interpretation of the model. The toxicophore elements, at least determined by the molecules used in this study, are a pattern of H-bond acceptor groups, presence of a H-bond donor and H-bond acceptor regions at ∼15 Å distance and a hydrophobic and H-bond acceptor interacting regions separated by a distance of ∼12.4 Å. Moreover, the dimensions of the molecule play a role in its recognition as a myelotoxic compound.
authors.affiliation: Attrition Reducing Technologies, Nerviano, Italy. patrizia.crivori@nervianoms.com
authors.forename: Patrizia
authors.initials: P
authors.lastname: Crivori
authors.forename: Giulia
authors.initials: G
authors.lastname: Pennella
authors.forename: Miriam
authors.initials: M
authors.lastname: Magistrelli
authors.forename: Pietro
authors.initials: P
authors.lastname: Grossi
authors.forename: Anna Maria
authors.initials: AM
authors.lastname: Giusti
mesh_terms: D001853:Bone Marrow / Q000187:drug effects*; D003198:Computer Simulation; D064420:Drug-Related Side Effects and Adverse Reactions; D006410:Hematopoiesis / Q000187:drug effects*; D006801:Humans; D008954:Models, Biological; D008958:Models, Molecular; D008968:Molecular Conformation; D004364:Pharmaceutical Preparations / Q000737:chemistry; D025341:Principal Component Analysis
chemical_list: D004364:Pharmaceutical Preparations