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Predicting Pharmacokinetic Properties of Potential Anticancer Agents via Their Chromatographic Behavior on Different Reversed Phase Materials.

ABSTRACT: The Quantitative Structure-Activity Relationship (QSAR) methodology was used to predict biological properties, i.e., the blood-brain distribution (log BB), fraction unbounded in the brain (f_u,brain), water-skin permeation (log K_p), binding to human plasma proteins (log K_a,HSA), and intestinal permeability (Caco-2), for three classes of fused azaisocytosine-containing congeners that were considered and tested as promising drug candidates. The compounds were characterized by lipophilic, structural, and electronic descriptors, i.e., chromatographic retention, topological polar surface area, polarizability, and molecular weight. Different reversed-phase liquid chromatography techniques were used to determine the chromatographic lipophilicity of the compounds that were tested, i.e., micellar liquid chromatography (MLC) with the ODS-2 column and polyoxyethylene lauryl ether (Brij 35) as the effluent component, an immobilized artificial membrane (IAM) chromatography with phosphatidylcholine column (IAM.PC.DD2) and chromatography with end-capped octadecylsilyl (ODS) column using aqueous solutions of acetonitrile as the mobile phases. Using multiple linear regression, we derived the statistically significant quantitative structure-activity relationships. All these QSAR equations were validated and were found to be very good. The investigations highlight the significance and possibilities of liquid chromatographic techniques with three different reversed-phase materials and QSARs methods in predicting the pharmacokinetic properties of our important organic compounds and reducing unethical animal testing.

SUBMITTER: Janicka M

PROVIDER: S-EPMC8072580 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:AimTo investigate the pharmacokinetics and disposition of simmitecan (L-P) that was a water-soluble ester prodrug of chimmitecan (L-2-Z) with potent anti-tumor activities in different experimental animals, and to assess its drug-drug interaction potential.MethodsSD rats were injected with a single iv bolus doses of L-P (3.75, 7.5 and 15 mg/kg). The pharmacokinetics, tissue distribution, excretion and metabolism of L-P and its active metabolite L-2-Z were studied through quantitative measurements and metabolite profiling with LC/MS. The binding of L-P and L-2-Z to rat plasma proteins was examined using an ultrafiltration method. Systemic exposures of beagle dogs to L-P as well as drug distribution in tumors of the nude mice xenograft model of human hepatic cancer SMMC-7721 cells were also examined. The metabolism of L-P by liver mcirosomal carboxylesterase in vitro was investigated in different species. The effects of L-P and L-2-Z on cytochrome P450 enzymes were examined using commercial screening kits.ResultsThe in vivo biotransformation of L-P to L-2-Z showed a significant species difference, with a mean elimination half-life t1/2 of approximately 1.4 h in rats and 1.9 h in dogs. The systemic exposure levels of L-P and L-2-Z were increased in a dose-dependent manner. In rats, approximately 66% of L-P and 79% of L-2-Z were bound to plasma proteins. In rats and the nude mice bearing human hepatic cancers, most organ tissues had significantly higher concentrations of L-P than the corresponding plasma levels. In the tumor tissues, the L-P levels were comparable to those of plasma, whereas the L-2-Z levels were lower than the L-P levels. In rats, L-P was eliminated mainly via biliary excretion, but metabolism played an important role in elimination of the intact L-P. Finally, L-P and L-2-Z exerted moderate inhibition on the activity of CYP3A4 in vitro.ConclusionL-P and L-2-Z have relatively short elimination half-lives and L-P is mainly eliminated via biliary excretion. The species difference in the conversion of L-P to L-2-Z and potential drug-drug interactions due to inhibition of CYP3A4 should be considered in further studies.

Project description:There is a growing demand for the use of machine learning (ML) to derive fast-to-evaluate surrogate models of materials properties. In recent years, a broad array of materials property databases have emerged as part of a digital transformation of materials science. However, recent technological advances in ML are not fully exploited because of the insufficient volume and diversity of materials data. An ML framework called "transfer learning" has considerable potential to overcome the problem of limited amounts of materials data. Transfer learning relies on the concept that various property types, such as physical, chemical, electronic, thermodynamic, and mechanical properties, are physically interrelated. For a given target property to be predicted from a limited supply of training data, models of related proxy properties are pretrained using sufficient data; these models capture common features relevant to the target task. Repurposing of such machine-acquired features on the target task yields outstanding prediction performance even with exceedingly small data sets, as if highly experienced human experts can make rational inferences even for considerably less experienced tasks. In this study, to facilitate widespread use of transfer learning, we develop a pretrained model library called XenonPy.MDL. In this first release, the library comprises more than 140?000 pretrained models for various properties of small molecules, polymers, and inorganic crystalline materials. Along with these pretrained models, we describe some outstanding successes of transfer learning in different scenarios such as building models with only dozens of materials data, increasing the ability of extrapolative prediction through a strategic model transfer, and so on. Remarkably, transfer learning has autonomously identified rather nontrivial transferability across different properties transcending the different disciplines of materials science; for example, our analysis has revealed underlying bridges between small molecules and polymers and between organic and inorganic chemistry.

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Predicting Pharmacokinetic Properties of Potential Anticancer Agents via Their Chromatographic Behavior on Different Reversed Phase Materials.

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