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.

Dosage Strategy of Linezolid According to the Trough Concentration Target and Renal Function in Chinese Critically Ill Patients

“…Thus, it is likely that increased linezolid exposure is the underlying reason for the higher risk of linezolid-related toxicity in patients with renal impairment. In addition, while renal function also plays a critical role in the clearance of the major metabolites of linezolid ( Souza et al, 2020 ), linezolid and its major metabolites share a chemical feature (aniline functional group) which medicinal chemistry has demonstrated to be a risk factor for myelosuppression ( Crivori et al, 2011 ; Stepan et al, 2011 ).…”

Accumulation of Major Linezolid Metabolites in Patients with Renal Impairment

“…Furthermore, the aniline functional group present in linezolid and its metabolites is an important structural alert in medicinal chemistry for myelosuppression risk (see Fig. S2 in the supplemental material) (15,16). Chemical standards for linezolid have not been readily available, which contributed to the limited assessment of its toxicology.…”

Using Bioinformatic Approaches to Identify Pathways Targeted by Human Leukemogens

“…To the best of our knowledge, structural similarity among leukemogens has not been reported. A recent study showed that in vitro myelotoxicity of chemical compounds could be predicted from molecular structure using in silico computational modeling [66]. Since the chemical leukemogens in our study cluster independently of structure and major mechanism of action (alkylating agent, topoisomerase II inhibitor, etc.…”