Christophe Francis Robert Nestor Buyck

Extending kinome coverage by analysis of kinase inhibitor broad profiling data

The explored kinome was extended with broad profiling using the DiscoveRx and Millipore assay panels. The analysis of the profiling of 3368 selected inhibitors on 456 kinases in the DiscoveRx format delivered several insights. First, the coverage depended on the threshold of the selectivity parameter. Second, the relation between hit confirmation rates and inhibitor selectivity showed unexpectedly that higher selectivity can increase the likelihood of false positives. Third, comparing the coverage of a focused to that of a random library showed that the design based on a maximum number of scaffolds was superior to a limited number of scaffolds. Therefore, selective compounds can be used in target validation, enable the jumpstarting of new kinase drug discovery projects, and chart new biological space via phenotypic screening.

Potent peptidic fusion inhibitors of influenza virus

Broadly reactive drugs for flu Drugs for influenza are limited. For those available, viral resistance is rife. Part of the problem is that the virus is constantly mutating. Kadam et al. tested the cell entry stage of the virus life cycle as a drug target (see the Perspective by Whitehead). Cell entry is mediated by the major surface glycoprotein hemagglutinin (HA). This stage can be blocked by broadly neutralizing antibodies binding to HA. The authors generated small cyclic peptides that bind to the same sites on HA as the antibodies and mimic their activity. The peptides are cheap and easy to synthesize, are nontoxic to mice, and prevented infection of cells by many types of influenza virus. Science, this issue p. 496; see also p. 450 Peptide mimics of broadly neutralizing antibodies target the cell fusion stem region of the virus hemagglutinin and have potential as drugs. Influenza therapeutics with new targets and mechanisms of action are urgently needed to combat potential pandemics, emerging viruses, and constantly mutating strains in circulation. We report here on the design and structural characterization of potent peptidic inhibitors of influenza hemagglutinin. The peptide design was based on complementarity-determining region loops of human broadly neutralizing antibodies against the hemagglutinin (FI6v3 and CR9114). The optimized peptides exhibit nanomolar affinity and neutralization against influenza A group 1 viruses, including the 2009 H1N1 pandemic and avian H5N1 strains. The peptide inhibitors bind to the highly conserved stem epitope and block the low pH–induced conformational rearrangements associated with membrane fusion. These peptidic compounds and their advantageous biological properties should accelerate the development of new small molecule– and peptide-based therapeutics against influenza virus.

A permutational-splitting sample procedure to quantify expert opinion on clusters of chemical compounds using high-dimensional data

Expert opinion plays an important role when selecting promising clusters of chemical compounds in the drug discovery process. We propose a method to quantify these qualitative assessments using hierarchical models. However, with the most commonly available computing resources, the high dimensionality of the vectors of fixed effects and correlated responses renders maximum likelihood unfeasible in this scenario. We devise a reliable procedure to tackle this problem and show, using theoretical arguments and simulations, that the new methodology compares favorably with maximum likelihood, when the latter option is available. The approach was motivated by a case study, which we present and analyze.

A new modeling approach for quantifying expert opinion in the drug discovery process

Expert opinion plays an important role when choosing clusters of chemical compounds for further investigation. Often, the process by which the clusters are assigned to the experts for evaluation, the so-called selection process, and the qualitative ratings given by the experts to the clusters (chosen/not chosen) need to be jointly modeled to avoid bias. This approach is referred to as the joint modeling approach. However, misspecifying the selection model may impact the estimation and inferences on parameters in the rating model, which are of most scientific interest. We propose to incorporate the selection process into the analysis by adding a new set of random effects to the rating model and, in this way, avoid the need to model it parametrically. This approach is referred to as the combined model approach. Through simulations, the performance of the combined and joint models was compared in terms of bias and confidence interval coverage. The estimates from the combined model were nearly unbiased, and the derived confidence intervals had coverage probability around 95% in all scenarios considered. In contrast, the estimates from the joint model were severely biased under some form of misspecification of the selection model, and fitting the model was often numerically challenging. The results show that the combined model may offer a safer alternative on which to base inferences when there are doubts about the validity of the selection model. Importantly, thanks to its greater numerical stability, the combined model may outperform the joint model even when the latter is correctly specified.

Protocols for the Design of Kinase-Focused Compound Libraries

Protocols for the design of kinase‐focused compound libraries are presented. Kinase‐focused compound libraries can be differentiated based on the design goal. Depending on whether the library should be a discovery library specific for one particular kinase, a general discovery library for multiple distinct kinase projects, or even phenotypic screening, there exists today a variety of in silico methods to design candidate compound libraries. We address the following scenarios: 1) Datamining of SAR databases and kinase focused vendor catalogues; 2) Predictions and virtual screening; 3) Structure‐based design of combinatorial kinase inhibitors; 4) Design of covalent kinase inhibitors; 5) Design of macrocyclic kinase inhibitors; and 6) Design of allosteric kinase inhibitors and activators.

Impact of selection bias on the evaluation of clusters of chemical compounds in the drug discovery process.

Expert opinion plays an important role when selecting promising clusters of chemical compounds in the drug discovery process. Indeed, experts can qualitatively assess the potential of each cluster, and with appropriate statistical methods, these qualitative assessments can be quantified into a success probability for each of them. However, one crucial element often overlooked is the procedure by which the clusters are assigned to/selected by the experts for evaluation. In the present work, the impact such a procedure may have on the statistical analysis and the entire evaluation process is studied. It has been shown that some implementations of the selection procedure may seriously compromise the validity of the evaluation even when the rating and selection processes are independent. Consequently, the fully random allocation of the clusters to the experts is strongly advocated.