Scaffold fragmentation and substructure hopping reveal potential, robustness, and limits of computer-aided pattern analysis (C@PA)

Vigneshwaran Namasivayam; Katja Silbermann; Jens Pahnke; Michael Wiese; Sven Marcel Stefan

doi:10.1016/j.csbj.2021.05.018

Scaffold fragmentation and substructure hopping reveal potential, robustness, and limits of computer-aided pattern analysis (C@PA)

Comput Struct Biotechnol J. 2021 May 10;19:3269-3283. doi: 10.1016/j.csbj.2021.05.018. eCollection 2021.

Authors

Vigneshwaran Namasivayam¹, Katja Silbermann¹, Jens Pahnke^{2

3

4

5}, Michael Wiese¹, Sven Marcel Stefan^{1

2

6}

Affiliations

¹ Department of Pharmaceutical and Cellbiological Chemistry, Pharmaceutical Institute, University of Bonn, An der Immenburg 4, 53121 Bonn, Germany.
² Department of Neuro-/Pathology, University of Oslo and Oslo University Hospital, Sognsvannsveien 20, 0372 Oslo, Norway.
³ LIED, University of Lübeck, Ratzenburger Allee 160, 23538 Lübeck, Germany.
⁴ Department of Pharmacology, Faculty of Medicine, University of Latvia, Jelgavas iela 1, 1004 Rīga, Latvia.
⁵ Department of Bioorganic Chemistry, Leibniz-Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany.
⁶ Cancer Drug Resistance and Stem Cell Program, University of Sydney, Kolling Builging, 10 Westbourne Street, Sydney, New South Wales 2065, Australia.

Abstract

Computer-aided pattern analysis (C@PA) was recently presented as a powerful tool to predict multitarget ABC transporter inhibitors. The backbone of this computational methodology was the statistical analysis of frequently occurring molecular features amongst a fixed set of reported small-molecules that had been evaluated toward ABCB1, ABCC1, and ABCG2. As a result, negative and positive patterns were elucidated, and secondary positive substructures could be suggested that complemented the multitarget fingerprints. Elevating C@PA to a non-statistical and exploratory level, the concluded secondary positive patterns were extended with potential positive substructures to improve C@PA's prediction capabilities and to explore its robustness. A small-set compound library of known ABCC1 inhibitors with a known hit rate for triple ABCB1, ABCC1, and ABCG2 inhibition was taken to virtually screen for the extended positive patterns. In total, 846 potential broad-spectrum ABCB1, ABCC1, and ABCG2 inhibitors resulted, from which 10 have been purchased and biologically evaluated. Our approach revealed 4 novel multitarget ABCB1, ABCC1, and ABCG2 inhibitors with a biological hit rate of 40%, but with a slightly lower inhibitory power than derived from the original C@PA. This is the very first report about discovering novel broad-spectrum inhibitors against the most prominent ABC transporters by improving C@PA.

Keywords: ABC transporter, ATP-binding cassette transporter; ABCB1 (P-gp); ABCC1 (MRP1); ABCG2 (BCRP); ATP, adenosine-triphosphate; Alzheimer's disease (AD); BCRP, breast cancer resistance protein (ABCG2); C@PA, computer-aided pattern analysis; F1–5, pharmacophore features 1–5; IC50, half-maximal inhibition concentration; MDR, multidrug resistance; MOE, molecular operating environment; MRP1, multidrug resistance-associated protein 1 (ABCC1); Multidrug resistance (MDR); Multitarget fingerprints; P-gp, P-glycoprotein (ABCB1); Pan-ABC inhibition / antagonism / blockage (PANABC); Pattern analysis (C@PA); SEM, standard error of the mean; SMILES, simplified molecular input line entry specification; Tc, Tanimotto coefficient; Triple / multitarget / broad-spectrum / promiscuous inhibitor / antagonist; Under-studied ABC transporters (e.g., ABCA7); Well-studied ABC transporters; calcein AM, calcein acetoxymethyl.