Protein-protein interactions (PPIs) may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI-HitProfiler is freely available on request from our CDithem platform website, www.CDithem.com. Protein-protein interactions (PPIs) are essential to life and various diseases states are associated with aberrant PPIs. Therefore significant efforts are dedicated to this new class of therapeutic targets. Even though it might not be possible to modulate the estimated 650,000 PPIs that regulate human life with drug-like compounds, a sizeable number of PPI should be druggable. Only 10-15% of the human genome is thought to be druggable with around 1000-3000 druggable protein targets. A hypothetical similar ratio for PPIs would bring the number of druggable PPIs to about 65,000, although no data can yet support such a hypothesis. PPI have been historically intricate to tackle with standard experimental and virtual screening techniques, possibly because of the shift in the chemical space between today's chemical libraries and PPI physico-chemical requirements. Therefore, one possible avenue to circumvent this conundrum is to design focused libraries enriched in putative PPI inhibitors. Here, we show how chemoinformatics can assist library design by learning physico-chemical rules from a data set of known PPI inhibitors and their comparison with regular drugs. Our study shows the importance of specific molecular shapes and a privileged number of aromatic bonds.

Insulin-degrading enzyme (IDE) is a protease that cleaves insulin and other bioactive peptides such as amyloid-β. Knockout and genetic studies have linked IDE to Alzheimer’s disease and type-2 diabetes. As the major insulin-degrading protease, IDE is a candidate drug target in diabetes. Here we have used kinetic target-guided synthesis to design the first catalytic site inhibitor of IDE suitable for in vivo studies (BDM44768). Crystallographic and small angle X-ray scattering analyses show that it locks IDE in a closed conformation. Among a panel of metalloproteases, BDM44768 selectively inhibits IDE. Acute treatment of mice with BDM44768 increases insulin signalling and surprisingly impairs glucose tolerance in an IDE-dependent manner. These results confirm that IDE is involved in pathways that modulate short-term glucose homeostasis, but casts doubt on the general usefulness of the inhibition of IDE catalytic activity to treat diabetes.

In the field of gene expression analysis, DNA microarray technology is having a major impact on many different areas including toxicology. For instance, a number of studies have shown that transcription profiling can generate the information needed to assign a compound to a mode-of-action class. In this study, we investigated whether compounds inducing similar toxicological endpoints produce similar changes in gene expression. In vitro primary rat hepatocytes were exposed to 11 different hepatotoxicants: acetaminophen, amiodarone, clofibrate, erythromycin estolate, isoniazid, α-naphtylylisothiocyanate, β-naphtoflavone, 4-pentenoic acid, phenobarbital, tetracycline, and zileuton. These molecules were selected on the basis of their variety of hepatocellular effects observed such as necrosis, cholestasis, steatosis, and induction of CYP P450 enzymes. We used a low-density DNA microarray containing 59 genes chosen as relevant toxic and metabolic markers. The in vitro gene expression data generated in this study were generally in good agreement with the literature, which mainly concerns in vivo data. Furthermore, gene expression profiles observed in this study have been confirmed for several genes by real-time PCR assays. All the tested drugs generated a specific gene expression profile. Our results show that even with a relatively limited gene set, gene expression profiling allows a certain degree of classification of compounds with similar hepatocellular toxicities such as cholestasis, necrosis. The clustering analysis revealed that the compounds known to cause steatosis were linked, suggesting that they functionally regulate similar genes and possibly act through the same mechanisms of action. On the other hand, the drugs inducing necrosis and cholestasis were pooled in the same cluster. The drugs arbitrarily classified as the CYP450 inducers formed individual clusters. In conclusion, this study suggests that low-density microarrays could be useful in toxicological studies.

Genome-wide association studies (GWASs) have identified 19 susceptibility loci for Alzheimer’s disease (AD). However, understanding how these genes are involved in the pathophysiology of AD is one of the main challenges of the “post-GWAS” era. At least 123 genes are located within the 19 susceptibility loci; hence, a conventional approach (studying the genes one by one) would not be time- and cost-effective. We therefore developed a genome-wide, high-content siRNA screening approach and used it to assess the functional impact of gene under-expression on APP metabolism. We found that 832 genes modulated APP metabolism. Eight of these genes were located within AD susceptibility loci. Only FERMT2 (a β3-integrin co-activator) was also significantly associated with a variation in cerebrospinal fluid Aβ peptide levels in 2886 AD cases. Lastly, we showed that the under-expression of FERMT2 increases Aβ peptide production by raising levels of mature APP at the cell surface and facilitating its recycling. Taken as a whole, our data suggest that FERMT2 modulates the AD risk by regulating APP metabolism and Aβ peptide production.