An increasingly competitive pharmaceutical market demands improvement in the efficiency and probability of drug candidate discovery. Usually these new drug candidates are targeted for oral administration, so a detailed understanding of the molecular-level properties that relate to optimal pharmacokinetics is a critical step toward improving the probability of selecting successful clinical candidates. Although the characteristics of druglike molecules have been previously discussed in the literature, the importance of this topic sustains a continued interest for additional perspective and further detailed statistical analyses. In this contribution, we approach the analysis from the perspective of profiling distinguishing features of orally administered drugs. We have compiled both structural and route-administration information for a total of 1729 marketed drugs to provide a solid basis for developing a new perspective on the characteristics of over 1000 orally administered drugs. The molecular properties and most commonly occurring structural elements are statistically analyzed to capture the differences between routes of administration, as well as between marketed drugs and SAR or clinical compounds. We find that, with respect to other routes of administration, oral drugs tend to be lighter and have fewer H-bond donors, acceptors, and rotatable bonds than drugs with other routes of administration. These differences are particularly pronounced when comparing the mean values for oral vs injectable drugs. We also demonstrate that the mean property values for oral drugs do not vary substantially with respect to launch date, suggesting that the range of acceptable oral properties is independent of synthetic complexity or targeted receptor. Finally, we note that, while these properties are descriptive of each class, they are not necessarily predictive of what class any particular drug will reside in, since there is significant overlap in the acceptable ranges found for each drug class.
This article describes a set of 275 rules, developed over an 18-year period, used to identify compounds that may interfere with biological assays, allowing their removal from screening sets. Reasons for rejection include reactivity (e.g., acyl halides), interference with assay measurements (fluorescence, absorbance, quenching), activities that damage proteins (oxidizers, detergents), instability (e.g., latent aldehydes), and lack of druggability (e.g., compounds lacking both oxygen and nitrogen). The structural queries were profiled for frequency of occurrence in druglike and nondruglike compound sets and were extensively reviewed by a panel of experienced medicinal chemists. As a means of profiling the rules and as a filter in its own right, an index of biological promiscuity was developed. The 584 gene targets with screening data at Lilly were assigned to 17 subfamilies, and the number of subfamilies at which a compound was active was used as a promiscuity index. For certain compounds, promiscuous activity disappeared after sample repurification, indicating interference from occult contaminants. Because this type of interference is not amenable to substructure search, a "nuisance list" was developed to flag interfering compounds that passed the substructure rules.
An extended reduced graph approach (ErG) is presented that uses pharmacophore-type node descriptions to encode the relevant molecular properties. The basic idea of the method can be described as a hybrid approach of reduced graphs (Gillet et al. J. Chem. Inf. Comput. Sci. 2003, 43, 338-345) and binding property pairs (Kearsley et al. J. Chem. Inf. Comput. Sci. 1996, 36, 118-127). However, specific extension modifications to correctly describe the pharmacophoric properties, size, and shape of the molecules under study result in a very stable and good performance as compared to DAYLIGHT fingerprints (DFP). This is exemplified for 11 activity classes of the MDL Drug Data Report database, for which ErG performs as well or better than DFP in 10 cases. On the basis of the example data sets, the ability of ErG to switch from one chemotype to another (often referred to as "scaffold hopping") is highlighted. Additionally, possible pitfalls of reduced graph approaches as well as suitable solutions are discussed with the help of example structures. Overall, it is shown that ErG is a widely applicable method capable of identifying structurally diverse actives for a given active search query. This diversity is achieved by a high degree of molecular abstraction, which in turn results in a low dimensional descriptor vector that allows very low computation times for similarity searches.
In silico tools are regularly utilized for designing and prioritizing compounds to address challenges related to drug metabolism and pharmacokinetics (DMPK) during the process of drug discovery. P-Glycoprotein (P-gp) is a member of the ATP-binding cassette (ABC) transporters with broad substrate specificity that plays a significant role in absorption and distribution of drugs that are P-gp substrates. As a result, screening for P-gp transport has now become routine in the drug discovery process. Typically, bidirectional permeability assays are employed to assess in vitro P-gp efflux. In this article, we use P-gp as an example to illustrate a well-validated methodology to effectively integrate in silico and in vitro tools to identify and resolve key barriers during the early stages of drug discovery. A detailed account of development and application of in silico tools such as simple guidelines based on physicochemical properties and more complex quantitative structure-activity relationship (QSAR) models is provided. The tools were developed based on structurally diverse data for more than 2000 compounds generated using a robust P-gp substrate assay over the past several years. Analysis of physicochemical properties revealed a significantly lower proportion (<10%) of P-gp substrates among the compounds with topological polar surface area (TPSA) <60 Å(2) and the most basic cpKa <8. In contrast, this proportion of substrates was greater than 75% for compounds with TPSA >60 Å(2) and the most basic cpKa >8. Among the various QSAR models evaluated to predict P-gp efflux, the Bagging model provided optimum prediction performance for prospective validation based on chronological test sets. Four sequential versions of the model were built with increasing numbers of compounds to train the models as new data became available. Except for the first version with the smallest training set, the QSAR models exhibited robust prediction profiles with positive prediction values (PPV) and negative prediction values (NPV) exceeding 80%. The QSAR model demonstrated better concordance with the manual P-gp substrate assay than an automated P-gp substrate screen. The in silico and the in vitro tools have been effectively integrated during early stages of drug discovery to resolve P-gp-related challenges exemplified by several case studies. Key learning based on our experience with P-gp can be widely applicable across other DMPK-related challenges.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.