When Drug-Drug interactions occur, one drug is usually the victim and the other is the perpetrator. In other cases, the drug could be a victim of high pharmacogenetic variability, such as being metabolized by CYP2D6 or CYP2C19. The perpetrator compound could be involved in either inhibition (sometimes also time dependent) or induction of clearance enzymes.

Examples of Ameliorating compound series when the lead compound is a victim or possibly a perpetrator will be described.

Three examples:

An example of changing clearance pathways so that the pharmacogenetically variable CYP2D6 enzyme was not involved in the end, in the clearance of a kinase compound (victim) will be shown.

An example of changing a series of compounds (perpetrator) involved in metabolite-based inhibition (MBI), will be shown.

Lastly, the rational modification of compounds (perpetrator) initially involved in CYP3A induction via the PXR nuclear receptor so that the final compounds did not induce CYP3A, will be shown.

Until the advent of GPU accelerated molecular dynamics, alchemical free energy simulations had not been routinely applied in early stage drug discovery. While this GPU acceleration made such calculations more feasible, the complexity of the protocols and uncertainty regarding optimal parameters are a lingering problem.

In the recently released MOE 2019 a streamlined interface was added to set up free alchemical energy calculations dynamics simulations based on the Thermodynamic Integration (TI) method in AMBER 18. The workflow includes structure preparation, ligand parameterization, simulation planning and analysis. Free energy calculations are known to be extremely sensitive and have a high failure rate. We present an optimal simulation planning methodology that minimizes the expected error for a given system based on the available simulation resources (number of GPUs, nodes, etc.). Another major source of error is the simulation instability due to an imbalance between the electrostatic and van der Waals forces. Optimization of the AMBER soft-core potential resulted in much higher simulation stability and hence lower curvature of the potential function. In conjunction with Fejér numerical integration the evaluation of the alchemical integral can be achieved with spectral accuracy. The methodology was validated through the calculation of relative binding energies of 34 ligands of the p38 MAP Kinase and compared to experimental data.

Society has witnessed through both news/media and the internet, the dangers and challenges of microbial and antibiotic resistance to therapeutic drugs in the human health sector. Considerably less attention has been given to the very real problem of agrochemical resistance is as it pertains to agricultural pest and weed management. The development of herbicide resistance in weeds challenges the sustainability of global food security for future generations. Modern herbicide resistance management programs focus on new biological targets or alternative "poisons" to old biological targets in an effort to counter the development of resistance in field settings. The shortcoming of these applied research directives is that they often fail to address key fundamental questions: Can we create new agrochemical solutions or identify pre-existing solutions that are less prone to resistance? Can we algorithmically and methodologically anticipate and avoid target-site resistance (TSR) in the agrochemical industry?

With a focus on identifying chemistries less prone to encountering resistance, we have selected the PPO (protoporphyrinogen oxidase) class of herbicides for which numerous herbicide-resistant weed strains exist and for which multiple classes of chemistries are on the market. Using workflows and tools developed for structure-based drug design, we have combined homology modeling, computational resistance scanning, mutation analysis and catalytic competency criteria to identify and structurally model a putative collection of biologically viable PPOX2 SNPs (single-nucleotide polymorphisms). These computationally validated/selected SNPs were used in a multi-target / multi-ligand (candidate PPO inhibitor chemistries) virtual screening campaign. The poses for the PPO chemistries explored were comparatively analyzed for each SNP against wild-type based on (a) protein-ligand interaction fingerprints, (b) impact on binding score and (c) ligand-to-substrate volumetric overlap. Results of this work have been validated against known field mutant/chemistry green-house experiments. Conclusions drawn from the approach aim at determining or promoting chemical candidate selections with a lower predicted susceptibility to resistance. Future work may include validation through bio-screening against cloned protein targets (plant/weed) and development of resistant mutations in yeast/microbes for testing novel chemistries at the target site.

This work illustrates, to our knowledge, the first application of computational protein design algorithms to prospectively predict plausible resistance mutations in planta under herbicide pressure. The approach demonstrated here could serve the dual purpose of designing new agrochemical solutions with a lower susceptibility for TSR while enabling the re-purposing of old but available agrochemical solutions at the known site of resistance.