Far away from the lamppost

[No abstract available

A selective ATP-binding cassette subfamily G member 2 efflux inhibitor revealed via high-throughput flow cytometry

Chemotherapeutics tumor resistance is a principal reason for treatment failure, and clinical and experimental data indicate that multidrug transporters such as ATP-binding cassette (ABC) B1 and ABCG2 play a leading role by preventing cytotoxic intracellular drug concentrations. Functional efflux inhibition of existing chemotherapeutics by these pumps continues to present a promising approach for treatment. A contributing factor to the failure of existing inhibitors in clinical applications is limited understanding of specific substrate/inhibitor/pump interactions. We have identified selective efflux inhibitors by profiling multiple ABC transporters against a library of small molecules to find molecular probes to further explore such interactions. In our primary screening protocol using JC-1 as a dual-pump fluorescent reporter substrate, we identified a piperazine-substituted pyrazolo[1,5-a]pyrimidine substructure with promise for selective efflux inhibition. As a result of a focused structure-activity relationship (SAR)-driven chemistry effort, we describe compound 1 (CID44640177), an efflux inhibitor with selectivity toward ABCG2 over ABCB1. Compound 1 is also shown to potentiate the activity of mitoxantrone in vitro as well as preliminarily in vivo in an ABCG2-overexpressing tumor model. At least two analogues significantly reduce tumor size in combination with the chemotherapeutic topotecan. To our knowledge, low nanomolar chemoreversal activity coupled with direct evidence of efflux inhibition for ABCG2 is unprecedented

Erratum: Unexplored therapeutic opportunities in the human genome (Nature reviews. Drug discovery (2018) 17 5 (317-332))

This corrects the article DOI: 10.1038/nrd.2018.14

Unexplored therapeutic opportunities in the human genome

A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development. © 2018 Macmillan Publishers Limited, part of Springer Nature. All rights reserved

Advancing biological understanding and therapeutics discovery with small-molecule probes

Small-molecule probes can illuminate biological processes and aid in the assessment of emerging therapeutic targets by perturbing biological systems in a manner distinct from other experimental approaches. Despite the tremendous promise of chemical tools for investigating biology and disease, small-molecule probes were unavailable for most targets and pathways as recently as a decade ago. In 2005, the NIH launched the decade-long Molecular Libraries Program with the intent of innovating in and broadening access to small-molecule science. This Perspective describes how novel small-molecule probes identified through the program are enabling the exploration of biological pathways and therapeutic hypotheses not otherwise testable. These experiences illustrate how small-molecule probes can help bridge the chasm between biological research and the development of medicines but also highlight the need to innovate the science of therapeutic discovery

Surrogate data - a secure way to share corporate data.

of molecules, surrogate data, lipophilicity prediction Summary The privacy of chemical structure is of paramount importance for the industrial sector, in particular for the pharmaceutical industry. At the same time, companies handle large amounts of physico-chemical and biological data that could be shared in order to improve our molecular understanding of pharmacokinetic and toxicological properties, which could lead to improved predictivity and shorten the development time for drugs, in particular in the early phases of drug discovery. The current study provides some theoretical limits on the information required to produce reverse engineering of molecules from generated descriptors and demonstrates that the information content of molecules can be as low as less than one bit per atom. Thus theoretically just one descriptor can be used to completely disclose the molecular structure. Instead of sharing descriptors, we propose to share surrogate data. The sharing of surrogate data is nothing else but sharing of reliably predicted molecules. The use of surrogate data can provide the same information as the original set. We consider the practical application of this idea to predict lipophilicity of chemical com-pounds and we demonstrate that surrogate and real (original) data provides similar prediction ability. Thus, our proposed strategy makes it possible not only to share descriptors, but also complete collections o

Three-dimensional model of a selective theophylline-binding RNA molecule

We propose a three-dimensional (3D) model for an RNA molecule that selectively binds theophylline but not caffeine. This RNA, which was found using SELEX [Jenison, R.D., et al., Science (1994) 263:1425] is 10,000 times more specific for theophylline (Kd=320 nM) than for caffeine (Kd=3.5 mM), although the two ligands are identical except for a methyl group substituted at N7 (present only in caffeine). The binding affinity for ten xanthine-based ligands was used to derive a Comparative Molecular Field Analysis (CoMFA) model (R{sup 2} = 0.93 for 3 components, with cross-validated R{sup 2} of 0.73), using the SYBYL and GOLPE programs. A pharmacophoric map was generated to locate steric and electrostatic interactions between theophylline and the RNA binding site. This information was used to identify putative functional groups of the binding pocket and to generate distance constraints. Based on a model for the secondary structure (Jenison et al., idem), the 3D structure of this RNA was then generated using the following method: each helical region of the RNA molecule was treated as a rigid body; single-stranded loops with specific end-to-end distances were generated. The structures of RNA-xanthine complexes were studied using a modified Monte Carlo algorithm. The detailed structure of an RNA-ligand complex model, as well as possible explanations for the theophylline selectivity will be discussed

Glossary of terms used in computational drug design, part II (IUPAC Recommendations 2015)

Computational drug design is a rapidly changing field that plays an increasingly important role in medicinal chemistry. Since the publication of the first glossary in 1997, substantial changes have occurred in both medicinal chemistry and computational drug design. This has resulted in the use of many new terms and the consequent necessity to update the previous glossary. For this purpose a Working Party of eight experts was assembled. They produced explanatory definitions of more than 150 new and revised terms