J Chem Inf Model - Predicting myelosuppression of drugs from in silico models.


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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 ? distance and a hydrophobic and H-bond acceptor interacting regions separated by a distance of ~12.4 ?. Moreover, the dimensions of the molecule play a role in its recognition as a myelotoxic compound.

Resumo Limpo

anticanc agent target prolifer cell popul tumor well normal tissu can lead number side effect includ hematotox common doselimit toxic associ oncolog drug myelosuppress regard unaccept therapeut indic consid clinic risk also new target anticanc drug act specif tumor cell thus becom import evalu potenti toxic new therapeut human hematopoiet tissu preclin develop also anticip liabil earli drug discoveri achiev use silico model guid design new lead compound select analog reduc myelosuppress potenti henc purpos studi develop comput model abl predict potenti myelotox drug chemic structur data set analyz includ drug structur divers druglik space cover molecul investig use chemgp methodolog two set potenti relev descriptor model myelotox ie d volsurf d structur electrotopolog estat descriptor select princip compon analysi carri entir set data first two pcs abl discrimin highest least myelotox compound total accuraci quantit pls model develop correl select subset vitro hematotox data volsurf descriptor variabl select pls analysi result onelatentvari model r q inclus d descriptor pls analysi improv slight robust qualiti model predict pic valu drug includ model rmsep squar correl coeffici r furthermor order investig whether high myelotox compound character common structur featur taken consider design new candid drug entir data set analyz use grind toxicophorebas descriptor one toxicophor emerg interpret model toxicophor element least determin molecul use studi pattern hbond acceptor group presenc hbond donor hbond acceptor region distanc hydrophob hbond acceptor interact region separ distanc moreov dimens molecul play role recognit myelotox compound

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