Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit.

Structure-based methods that employ principles of de novo design can be used to construct small organic molecules from scratch using pre-existing fragment libraries to sample chemical space and are an important class of computational algorithms for drug-lead discovery. Here, we present a powerful new design method for DOCK6 that employs a Descriptor-Driven De Novo strategy (termed D3N) in which user-defined cheminformatics descriptors (and their target ranges) are calculated at each layer of growth using the open-source toolkit RDKit. The objective is to tailor ligand growth toward desirable regions of chemical space. The approach was extensively validated through: (1) comparison of cheminformatics descriptors computed using the new DOCK6/RDKit interface versus the standard Python/RDKit installation, (2) examination of descriptor distributions generated using D3N growth under different conditions (target ranges and environments), and (3) construction of ligands with very tight (pinpoint) descriptor ranges using clinically relevant compounds as a reference. Our testing confirms that the new DOCK6/RDKit integration is robust, showcases how the new D3N routines can be used to direct sampling around user-defined chemical spaces, and highlights the utility of on-the-fly descriptor calculations for ligand design to important drug targets.

Synthesis of 2,3-dihydrobenzo[b][1,4]dioxine-5-carboxamide and 3-oxo-3,4-dihydrobenzo[b][1,4]oxazine-8-carboxamide derivatives as PARP1 inhibitors.

Poly(ADP-ribose) polymerase 1 (PARP1), a widely explored anticancer drug target, plays an important role in single-strand DNA break repair processes. High-throughput virtual screening (HTVS) of a Maybridge small molecule library using the PARP1-benzimidazole-4-carboxamide co-crystal structure and pharmacophore model led to the identification of eleven compounds. These compounds were evaluated using recombinant PARP1 enzyme assay that resulted in the acquisition of three PARP1 inhibitors: 3 (IC50 = 12 µM), 4 (IC50 = 5.8 µM), and 10 (IC50 = 0.88 µM). Compound 4 (2,3-dihydro-1,4-benzodioxine-5-carboxamide) was selected as a lead and was subjected to further chemical modifications, involving analogue synthesis and scaffold hopping. These efforts led to the identification of (Z)-2-(4-hydroxybenzylidene)-3-oxo-3,4-dihydro-2H-benzo[b][1,4]oxazine-8-carboxamide (49, IC50 = 0.082 µM) as the most potent inhibitor of PARP1 from the series.

Cumulative risk assessment and environmental equity in air permitting: interpretation, methods, community participation and implementation of a unique statute.

In 2008, the statute authorizing the Minnesota Pollution Control Agency (MPCA) to issue air permits was amended to include a unique requirement to analyze and consider "cumulative levels and effects of past and current environmental pollution from all sources on the environment and residents of the geographic area within which the facility's emissions are likely to be deposited." Data describing the Statute Area suggest it is challenged by environmental and socioeconomic concerns, i.e., concerns which are often described by the phrase 'environmental equity'. With input from diverse stakeholders, the MPCA developed a methodology for implementing a cumulative levels and effects analysis when issuing air permits in the designated geographic area. A Process Document was created defining explicit steps a project proposer must complete in the analysis. An accompanying Reference Document compiles all available environmental health data relevant to the Statute Area that could be identified. The final cumulative levels and effects methodology is organized by health endpoint and identifies hazard, exposure and health indices that require further evaluation. The resulting assessment is summarized and presented to decision makers for consideration in the regulatory permitting process. We present a description of the methodology followed by a case study summary of the first air permit processed through the "cumulative levels and effects analysis".