Efficient drug lead discovery and optimization

Abstract

During the 1980s, advances in the abilities to perform computer simulations of chemical and biomolecular systems and to calculate free energy changes led to the expectation that such methodology would soon show great utility for guiding molecular design. Important potential applications included design of selective receptors, catalysts, and regulators of biological function including enzyme inhibitors. This time also saw the rise of high-throughput screening and combinatorial chemistry along with complementary computational methods for de novo design and virtual screening including docking. These technologies appeared poised to deliver diverse lead compounds for any biological target. As with many technological advances, realization of the expectations required significant additional effort and time. However, as summarized here, striking success has now been achieved for computer-aided drug lead generation and optimization. De novo design using both molecular growing and docking are illustrated for lead generation, and lead optimization features free energy perturbation calculations in conjunction with Monte Carlo statistical mechanics simulations for protein-inhibitor complexes in aqueous solution. The specific applications are to the discovery of non-nucleoside inhibitors of HIV reverse transcriptase (HIV-RT) and inhibitors of the binding of the proinflammatory cytokine MIF to its receptor CD74. A standard protocol is presented that includes scans for possible additions of small substituents to a molecular core, interchange of heterocycles, and focused optimization of substituents at one site. Initial leads with activities at low-micromolar concentrations have been advanced rapidly to low-nanomolar inhibitors.

Figure 1

Schematic outline for structure-based lead discovery and optimization.

Figure 2

Complex of HIV-RT with a non-nucleoside inhibitor (NNRTI) built using BOMB. The hydrogen bond with the oxygen atom of Lys101 is dashed.

Figure 3

(Left) Diverse inhibitors of MIF discovered by docking, purchase, and assaying. (Right) Computed image of the benzoisothiazolone, a 4-µM tautomerase inhibitor, bound to MIF. Binding features aryl-aryl interactions and hydrogen bonding.

Figure 4

(Left) A protein-ligand complex in a water droplet; typically, the ligand, 200–300 nearby residues, and 1000 water molecules are modeled. (Right) Thermodynamic cycle for relative free energies of binding. P is the receptor and X and Y are two ligands.

Figure 5

Heterocycle scan in the biHet-NH-3-Ph-U series: FEP results for relative ΔG_b (kcal/mol) and experimental anti-HIV activities (nM).

Figure 6

Heterocycle scan in the U-5Het-NH-4PhX series; FEP results for ΔG_b (kcal/mol) relative to the thiophene analogue and experimental anti-HIV activity.

Figure 7

The power of chlorine and methyl scans; experimental EC₅₀ values for anti-HIV activity.

Figure 8

(Left) FEP-computed changes in ΔG_b (kcal/mol) for replacement of the indicted hydrogens by chlorine. (Right) Snapshot of the complex of 4 bound to HIV-RT from MC/FEP simulations.