Merging cultures and disciplines to create a drug discovery ecosystem at Virginia commonwealth university: Medicinal chemistry, structural biology, molecular and behavioral pharmacology and computational chemistry

Abstract

The Department of Medicinal Chemistry, together with the Institute for Structural Biology, Drug Discovery and Development, at Virginia Commonwealth University (VCU) has evolved, organically with quite a bit of bootstrapping, into a unique drug discovery ecosystem in response to the environment and culture of the university and the wider research enterprise. Each faculty member that joined the department and/or institute added a layer of expertise, technology and most importantly, innovation, that fertilized numerous collaborations within the University and with outside partners. Despite moderate institutional support with respect to a typical drug discovery enterprise, the VCU drug discovery ecosystem has built and maintained an impressive array of facilities and instrumentation for drug synthesis, drug characterization, biomolecular structural analysis and biophysical analysis, and pharmacological studies. Altogether, this ecosystem has had major impacts on numerous therapeutic areas, such as neurology, psychiatry, drugs of abuse, cancer, sickle cell disease, coagulopathy, inflammation, aging disorders and others. Novel tools and strategies for drug discovery, design and development have been developed at VCU in the last five decades; e.g., fundamental rational structure-activity relationship (SAR)-based drug design, structure-based drug design, orthosteric and allosteric drug design, design of multi-functional agents towards polypharmacy outcomes, principles on designing glycosaminoglycans as drugs, and computational tools and algorithms for quantitative SAR (QSAR) and understanding the roles of water and the hydrophobic effect.

Keywords: Allosteric effectors of hemoglobin; Computational glycomics; Drug Discrimination; Drug discovery ecosystem; Experimental structural biology; G protein-coupled receptors; High-throughput screening; Quantitative structure-activity relationships; Structure-based drug discovery.

Fig. 1.

Cartoon representation of the VCU Medicinal Chemistry Drug Discovery Ecosystem. The various components – synthetic chemistry, protein production, assays and screening, behavioral pharmacology, structural biology, computational chemistry, etc. – are illustrated in the context of an environmental system. VCU faculty members contributing to these components are indicated.

Fig. 2.

Selected instrumentation in the VCU Department of Medicinal Chemistry and Institute for Structural Biology, Drug Discovery and Development. a) 35 liter sterilizable New Brunswick 510 fermenter with an associated Beckman flow centrifuge and AKTA Pure fast protein liquid chromatography (FPLC) system; b) Biotek Cytation 5 cell imaging multimode reader system; c) Labcyte Echo 550 liquid handler for nL-level dispensing; d) Molecular Devices FlexStation 3 Multi-Mode Microplate Reader; e) Nanotemper NT-Automated for microscale thermophoresis (MST) to measure interactions and affinity; f) Thermo Scientific Orbitrap Fusion Lumos Mass Spectrometer; g) Malvern Microcal PEAQ Isothermal Calorimeter (ITC); h) ARI Crystal Gryphon robot for crystallization fitted with Minstrel/Gallery imaging system; and i) Rigaku MicroMax-007HF X-ray Generator with VariMax-HF Arc Optics/Hybrid Photon Counter, Eiger R 4M Detector and AFC11 Goniometer.

Fig. 3.

The library of unique sulfated non-saccharide glycosaminoglycan mimetics synthesized in the Desai laboratory. The characteristic feature of these molecules is their three-dimensional sulfated aromatic scaffold, which induces target selectivity as well as high aqueous solubility. The library includes mono-sulfated to poly-sulfated molecules belonging to the flavonoid, benzofuran, isoquinoline, quinazolinone, glucoside and inositol scaffolds. Distinct members of this library have been found to exhibit distinct biological activities including antithrombotic, anti-cancer, anti-inflammatory, and anti-viral. These have been documented in numerous papers from the group in collaboration with biologists.

Fig. 4.

Figure 4. Drug Discrimination or Stimulus Generalization. In these studies. animals (rats) are trained, using a two-lever operant apparatus, to discriminate (i.e., recognize) a specific training drug / drug- dose stimulus (by responding on Lever 1) from a non-drug condition (by responding on Lever 2). Animals are then administered a novel test drug under non-reinforcement conditions (i.e., without reward) to determine how they will respond; (that is, does the test drug produce a stimulus effect similar, and in a dose-responsive manner, to the training drug (i.e., by responding on Lever 1)? Or, is the response different than that of the training drug (as indicated by their responding on Lever 2, not responding, or disruption of behavior). Animals tested during test sessions are free to select either lever (as indicated by the animal and its shadow), and respond on one of the two levers; that is, the animals “decide ” (as indicated by “? ”) whether the test drug is perceived to produce a stimulus effect similar to or distinct from the training drug condition. See Glennon and Young [106] for a detailed explanation. Image courtesy of R. A. Glennon.

Fig. 5.

The X-ray crystal structure of deoxygenated hemoglobin. Abraham and Safo have exploited this structure to design numerous allosteric effectors and anti-sickling agents.

Fig. 6.

Portoghese’s “message-address” concept. Yan Zhang’s research group uses this concept to design selective ligands for different type opioid receptor types by installing unique structural features that recognize the “address” domain (in green) of the receptor. For the ligands, they carry the same “message ” portions (in red) to recognize the common “message ” domains in the opioid receptors (in brown) and modulate their function, while the different “address ” portions of the ligands bind to differentiated “address ” domains of the receptors to achieve high selectivity. This is further demonstrated by the delta-selective opioid receptor ligand NTI and the kappa-selective opioid receptor ligand GNTI from Portoghese’s lab.

Fig. 7.

Protein structure analysis and prediction. (Left) 3D HINT maps showing interactions between aspartate residues and their local environments. The contours represent favorable polar interactions (blue), unfavorable polar interactions (red), unfavorable hydrophobic interactions (purple). The large numbers, 32, 58, 394 and 396, are specific cluster names referenced to the cluster’s exemplar (residue closest to its centroid) from an ordered list [189]. The fraction of each numbered residue compared to all with the same backbone angles is indicated. (Right) The computed titration curve of all ~40,000 aspartates in the data set.