Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach

Abstract

It is generally recognized that drug discovery and development are very time and resources consuming processes. There is an ever growing effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization. In biomedical arena, computer-aided or in silico design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues. Commonly used computational approaches include ligand-based drug design (pharmacophore, a 3D spatial arrangement of chemical features essential for biological activity), structure-based drug design (drug-target docking), and quantitative structure-activity and quantitative structure-property relationships. Regulatory agencies as well as pharmaceutical industry are actively involved in development of computational tools that will improve effectiveness and efficiency of drug discovery and development process, decrease use of animals, and increase predictability. It is expected that the power of CADDD will grow as the technology continues to evolve.

Figure 1

Comparison of traditional and virtual screening in terms of expected cost and time requirements.

Figure 2

Modern drug discovery and development process including prominent role of computational modeling. Figure was reprinted from Drug Discovery Today 11: 326–333 (2006), “Integrating molecular design resources within modern drug discovery research: the Roche experience”, by M. Stahl, W. Guba and M. Kansy [15], with permission from Elsevier.

Figure 3

Estrogen receptor beta (ERβ) agonist pharmacophore. Pharmacophore model of ERβ agonist consisting of two hydrogen bond donor (HD), one aromatic (AR) and one hydrophobic (H) descriptors, was developed using Catalyst 4.11 (http://www.accelrys.com/products/catalyst/) based on published IC₅₀ data from Chemical Sciences Group at Wyeth Research [–27]. Descriptors are represented by tolerance spheres around a centroid. Some descriptors (HD, AR) have directionality and are thus represented as vectors with two tolerance spheres each. Top figure represents the derived pharmacophore with inter-descriptor distances in Angstroms. Values for angles were omitted for visual clarity. Bottom figure represents an overlay of resveratrol with the derived pharmacophore. Goodness of fit of chemical structure to the pharmacophore is based on its having the functional moities complimentary to the pharmacophore descriptors and their closeness to the centroid and tolerance spheres of the latter in the 3-dimensional space.

Figure 4. Docking

a.) Ligand binding of p38 mitogen-activated protein kinase with inhibitor BIRB796 (PDB code: 1KV2), including its electrostatic potential surface and b.) enlarged view. c.) hydrogen bonding and van der Waals interactions are depicted as darker and lighter dashed lines, respectively. d.) van der Waals, hydrogen bonding and electrostatic (same or opposite charges) energies as a function of inter-atomic distance (r_ij). Figure was reproduced from Nature Reviews Drug Discovery 3: 935–949 (2004), “Docking and scoring in virtual screening for drug discovery: methods and applications”, by D.B. Kitchen, H. Decornez, J.R. Furr, and J. Bajorath [28], with permission from Macmillan Magazines Ltd.

Figure 5

Binding of ERβ selective agonist, ERB-041 in the ligand binding domain of ERβ receptor. Shape of the binding site is represented by a Connolly surface. Two amino acid substitutions between ERα and ERβ ligand binding cavity are labeled accordingly. Figure was reprinted from Journal of Medicinal Chemistry 47: 5021–5040 (2004), “Design and synthesis of aryl diphenolic azoles as potent and selective estrogen receptor-β ligands”, by M.S. Malamas, E.S. Manas, R.E. McDevitt, I. Gunawan, Z.B. Xu, M.D. Collini, C.P. Miller, T. Dinh, R.A. Henderson, J.C. Keith Jr., and H.A. Harris [25], with permission from the American Chemical Society.

Figure 6

Traditional and computational approaches to selection of the Maximum Recommended Starting Dose (MRSD) for Phase 1 clinical trials. Figure was reprinted from Regulatory Toxicology and Pharmacology 40: 185–206 (2004), “Estimating the safe and staring dose in phase I clinical trials and no observed effect level based on QSAR modeling of the human maximum recommended daily dose”, by J.F. Contrera, E.J. Matthews, N.L. Kruhlak, and R. D. Benz [79], with permission from Elsevier.