The biochemical half maximal inhibitory concentration (IC50) is the most commonly used metric for on-target activity in lead optimization. It is used to guide lead optimization, build large-scale chemogenomics analysis, off-target activity and toxicity models based on public data. However, the use of public biochemical IC50 data is problematic, because they are assay specific and comparable only under certain conditions. For large scale analysis it is not feasible to check each data entry manually and it is very tempting to mix all available IC50 values from public database even if assay information is not reported. As previously reported for Ki database analysis, we first analyzed the types of errors, the redundancy and the variability that can be found in ChEMBL IC50 database. For assessing the variability of IC50 data independently measured in two different labs at least ten IC50 data for identical protein-ligand systems against the same target were searched in ChEMBL. As a not sufficient number of cases of this type are available, the variability of IC50 data was assessed by comparing all pairs of independent IC50 measurements on identical protein-ligand systems. The standard deviation of IC50 data is only 25% larger than the standard deviation of Ki data, suggesting that mixing IC50 data from different assays, even not knowing assay conditions details, only adds a moderate amount of noise to the overall data. The standard deviation of public ChEMBL IC50 data, as expected, resulted greater than the standard deviation of in-house intra-laboratory/inter-day IC50 data. Augmenting mixed public IC50 data by public Ki data does not deteriorate the quality of the mixed IC50 data, if the Ki is corrected by an offset. For a broad dataset such as ChEMBL database a Ki- IC50 conversion factor of 2 was found to be the most reasonable.

The maximum achievable accuracy of in silico models depends on the quality of the experimental data. Consequently, experimental uncertainty defines a natural upper limit to the predictive performance possible. Models that yield errors smaller than the experimental uncertainty are necessarily overtrained. A reliable estimate of the experimental uncertainty is therefore of high importance to all originators and users of in silico models. The data deposited in ChEMBL was analyzed for reproducibility, i.e., the experimental uncertainty of independent measurements. Careful filtering of the data was required because ChEMBL contains unit-transcription errors, undifferentiated stereoisomers, and repeated citations of single measurements (90% of all pairs). The experimental uncertainty is estimated to yield a mean error of 0.44 pK(i) units, a standard deviation of 0.54 pK(i) units, and a median error of 0.34 pK(i) units. The maximum possible squared Pearson correlation coefficient (R(2)) on large data sets is estimated to be 0.81.

Diarrheal disease is responsible for 8.6% of global child mortality. Recent epidemiological studies found the protozoan parasite Cryptosporidium to be a leading cause of pediatric diarrhea with particularly grave impact on infants and immunocompromised individuals. There is neither a vaccine nor effective treatment. We establish a drug discovery process built on scalable phenotypic assays and mouse models that takes advantage of transgenic parasites. Screening a library of compounds with anti-parasitic activity we identified pyrazolopyridines as inhibitors of Cryptosporidium parvum and C. hominis. Oral treatment with the pyrazolopyridine KDU731 results in potent reduction in intestinal infection of immunocompromised mice. Treatment also leads to rapid resolution of diarrhea and dehydration in neonatal calves, a clinical model of cryptosporidiosis that closely resembles human infection. Our results suggest the Cryptosporidium lipid kinase PI(4)K as a target for pyrazolopyridines and warrant further preclinical evaluation of KDU731 as a drug candidate for the treatment of cryptosporidiosis.

A molecular understanding of drug resistance mechanisms enables surveillance of the effectiveness of new antimicrobial therapies during development and deployment in the field. We used conventional drug resistance selection as well as a regime of limiting dilution at early stages of drug treatment to probe two antimalarial imidazolopiperazines, KAF156 and GNF179. The latter approach permits isolation of low-fitness mutants that might otherwise be out-competed during selection. Whole-genome sequencing of 24 independently-derived resistant P. falciparum clones revealed four parasites with mutations in the known cyclic amine resistance locus (pfcarl), and a further 20 with mutations in two previously unreported P. falciparum drug resistance genes, an acetyl-CoA transporter (pfact) and a UDP-galactose transporter (pfugt). Mutations were validated both in vitro by CRISPR editing in P. falciparum, and in vivo by evolution of resistant P. berghei mutants. Both PfACT and PfUGT were localized to the endoplasmic reticulum by fluorescence microscopy. As mutations in pfact and pfugt conveyed resistance against additional unrelated chemical scaffolds, these genes are likely to be involved in broad mechanisms of antimalarial drug resistance.

Many commonly used, structurally diverse, drugs block the human ether-a-go-go-related gene (hERG) K(+) channel to cause acquired long QT syndrome, which can lead to sudden death via lethal cardiac arrhythmias. This undesirable side effect is a major hurdle in the development of safe drugs. To gain insight about the structure of hERG and the nature of drug block we have produced structural models of the channel pore domain, into each of which we have docked a set of 20 hERG blockers. In the absence of an experimentally determined three-dimensional structure of hERG, each of the models was validated against site-directed mutagenesis data. First, hERG models were produced of the open and closed channel states, based on homology with the prokaryotic K(+) channel crystal structures. The modeled complexes were in partial agreement with the mutagenesis data. To improve agreement with mutagenesis data, a KcsA-based model was refined by rotating the four copies of the S6 transmembrane helix half a residue position toward the C-terminus, so as to place all residues known to be involved in drug binding in positions lining the central cavity. This model produces complexes that are consistent with mutagenesis data for smaller, but not larger, ligands. Larger ligands could be accommodated following refinement of this model by enlarging the cavity using the inherent flexibility about the glycine hinge (Gly648) in S6, to produce results consistent with the experimental data for the majority of ligands tested.