A small-molecule PI3Kα activator for cardioprotection and neuroregeneration.

Harnessing the potential beneficial effects of kinase signalling through the generation of direct kinase activators remains an underexplored area of drug development1-5. This also applies to the PI3K signalling pathway, which has been extensively targeted by inhibitors for conditions with PI3K overactivation, such as cancer and immune dysregulation. Here we report the discovery of UCL-TRO-1938 (referred to as 1938 hereon), a small-molecule activator of the PI3Kα isoform, a crucial effector of growth factor signalling. 1938 allosterically activates PI3Kα through a distinct mechanism by enhancing multiple steps of the PI3Kα catalytic cycle and causes both local and global conformational changes in the PI3Kα structure. This compound is selective for PI3Kα over other PI3K isoforms and multiple protein and lipid kinases. It transiently activates PI3K signalling in all rodent and human cells tested, resulting in cellular responses such as proliferation and neurite outgrowth. In rodent models, acute treatment with 1938 provides cardioprotection from ischaemia-reperfusion injury and, after local administration, enhances nerve regeneration following nerve crush. This study identifies a chemical tool to directly probe the PI3Kα signalling pathway and a new approach to modulate PI3K activity, widening the therapeutic potential of targeting these enzymes through short-term activation for tissue protection and regeneration. Our findings illustrate the potential of activating kinases for therapeutic benefit, a currently largely untapped area of drug development.

Public-Private Partnerships: Compound and Data Sharing in Drug Discovery and Development.

Collaborative efforts between public and private entities such as academic institutions, governments, and pharmaceutical companies form an integral part of scientific research, and notable instances of such initiatives have been created within the life science community. Several examples of alliances exist with the broad goal of collaborating toward scientific advancement and improved public welfare. Such collaborations can be essential in catalyzing breaking areas of science within high-risk or global public health strategies that may have otherwise not progressed. A common term used to describe these alliances is public-private partnership (PPP). This review discusses different aspects of such partnerships in drug discovery/development and provides example applications as well as successful case studies. Specific areas that are covered include PPPs for sharing compounds at various phases of the drug discovery process-from compound collections for hit identification to sharing clinical candidates. Instances of PPPs to support better data integration and build better machine learning models are also discussed. The review also provides examples of PPPs that address the gap in knowledge or resources among involved parties and advance drug discovery, especially in disease areas with unfulfilled and/or social needs, like neurological disorders, cancer, and neglected and rare diseases.

Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies.

A significant challenge in high-throughput screening (HTS) campaigns is the identification of assay technology interference compounds. A Compound Interfering with an Assay Technology (CIAT) gives false readouts in many assays. CIATs are often considered viable hits and investigated in follow-up studies, thus impeding research and wasting resources. In this study, we developed a machine-learning (ML) model to predict CIATs for three assay technologies. The model was trained on known CIATs and non-CIATs (NCIATs) identified in artefact assays and described by their 2D structural descriptors. Usual methods identifying CIATs are based on statistical analysis of historical primary screening data and do not consider experimental assays identifying CIATs. Our results show successful prediction of CIATs for existing and novel compounds and provide a complementary and wider set of predicted CIATs compared to BSF, a published structure-independent model, and to the PAINS substructural filters. Our analysis is an example of how well-curated datasets can provide powerful predictive models despite their relatively small size.

Identification of indole inhibitors of human hematopoietic prostaglandin D2 synthase (hH-PGDS).

Human H-PGDS has shown promise as a potential target for anti-allergic and anti-inflammatory drugs. Here we describe the discovery of a novel class of indole inhibitors, identified through focused screening of 42,000 compounds and evaluated using a series of hit validation assays that included fluorescence polarization binding, 1D NMR, ITC and chromogenic enzymatic assays. Compounds with low nanomolar potency, favorable physico-chemical properties and inhibitory activity in human mast cells have been identified. In addition, our studies suggest that the active site of hH-PGDS can accommodate larger structural diversity than previously thought, such as the introduction of polar groups in the inner part of the binding pocket.

On the Relationship between Molecular Hit Rates in High-Throughput Screening and Molecular Descriptors.

High-throughput screening (HTS) is widely used in the pharmaceutical industry to identify novel chemical starting points for drug discovery projects. The current study focuses on the relationship between molecular hit rate in recent in-house HTS and four common molecular descriptors: lipophilicity (ClogP), size (heavy atom count, HEV), fraction of sp(3)-hybridized carbons (Fsp3), and fraction of molecular framework (f(MF)). The molecular hit rate is defined as the fraction of times the molecule has been assigned as active in the HTS campaigns where it has been screened. Beta-binomial statistical models were built to model the molecular hit rate as a function of these descriptors. The advantage of the beta-binomial statistical models is that the correlation between the descriptors is taken into account. Higher degree polynomial terms of the descriptors were also added into the beta-binomial statistic model to improve the model quality. The relative influence of different molecular descriptors on molecular hit rate has been estimated, taking into account that the descriptors are correlated to each other through applying beta-binomial statistical modeling. The results show that ClogP has the largest influence on the molecular hit rate, followed by Fsp3 and HEV. f(MF) has only a minor influence besides its correlation with the other molecular descriptors.

Comparative binding energy analysis for binding affinity and target selectivity prediction.

A major challenge in drug design is to obtain compounds that bind selectively to their target receptors and do not cause side-effects by binding to other similar receptors. Here, we investigate strategies for applying COMBINE (COMparative BINding Energy) analysis, in conjunction with PIPSA (Protein Interaction Property Similarity Analysis) and ligand docking methods, to address this problem. We evaluate these approaches by application to diverse sets of inhibitors of three structurally related serine proteases of medical relevance: thrombin, trypsin, and urokinase-type plasminogen activator (uPA). We generated target-specific scoring functions (COMBINE models) for the three targets using training sets of ligands with known inhibition constants and structures of their receptor-ligand complexes. These COMBINE models were compared with the PIPSA results and experimental data on receptor selectivity. These scoring functions highlight the ligand-receptor interactions that are particularly important for binding specificity for the different targets. To predict target selectivity in virtual screening, compounds were docked into the three protein binding sites using the program GOLD and the docking solutions were re-ranked with the target-specific scoring functions and computed electrostatic binding free energies. Limits in the accuracy of some of the docking solutions and difficulties in scoring them adversely affected the predictive ability of the target specific scoring functions. Nevertheless, the target-specific scoring functions enabled the selectivity of ligands to thrombin versus trypsin and uPA to be predicted.

Potent inhibitors of the Plasmodium falciparum enzymes plasmepsin I and II devoid of cathepsin D inhibitory activity.

The hemoglobin-degrading aspartic proteases plasmepsin I (Plm I) and plasmepsin II (Plm II) of the malaria parasite Plasmodium falciparum have lately emerged as putative drug targets. A series of C(2)-symmetric compounds encompassing the 1,2-dihydroxyethylene scaffold and a variety of elongated P1/P1' side chains were synthesized via microwave-assisted palladium-catalyzed coupling reactions. Binding affinity calculations with the linear interaction energy method and molecular dynamics simulations reproduced the experimental binding data obtained in a Plm II assay with very good accuracy. Bioactive conformations of the elongated P1/P1' chains were predicted and agreed essentially with a recent X-ray structure. The compounds exhibited picomolar to nanomolar inhibition constants for the plasmepsins and no measurable affinity to the human enzyme cathepsin D. Some of the compounds also demonstrated significant inhibition of parasite growth in cell culture. To the best of our knowledge, these plasmepsin inhibitors represent the most selective reported to date and constitute promising lead compounds for further optimization.

C2-symmetric inhibitors of Plasmodium falciparum plasmepsin II: synthesis and theoretical predictions.

A series of C(2)-symmetric compounds with a mannitol-based scaffold has been investigated, both theoretically and experimentally, as Plm II inhibitors. Four different stereoisomers with either benzyloxy or allyloxy P1/P1' side chains were studied. Computational ranking of the binding affinities of the eight compounds was carried out using the linear interaction energy (LIE) method relying on a complex previously determined by crystallography. Within both series of isomers the theoretical binding energies were in agreement with the enzymatic measurements, illustrating the power of the LIE method for the prediction of ligand affinities prior to synthesis. The structural models of the enzyme-inhibitor complexes obtained from the MD simulations provided a basis for interpretation of further structure-activity relationships. Hence, the affinity of a structurally similar ligand, but with a different P2/P2' substituent was examined using the same procedure. The predicted improvement in binding constant agreed well with experimental results.

The catalytic power of ketosteroid isomerase investigated by computer simulation.

Ketosteroid isomerase (KSI) catalyzes the isomerization of Delta(5)-3-ketosteroids and Delta(4)-3-ketosteroids at very high rates. Here we examine the principles underlying the catalytic efficiency of KSI by computer simulations using the empirical valence bond method in combination with molecular dynamics free energy perturbation simulations. The simulations reproduce available kinetic and structural data very well and allow us to examine several features of the catalytic mechanism in detail. It is found that about 60% of the rate enhancement is due to stabilization of the negatively charged dienolate intermediate by hydrogen bonding. The critical H-bond between Tyr16 and the intermediate is found to be a normal ionic H-bond with the preferred proton location on the tyrosine residue. The remaining 40% of the catalytic effect originates from a reduction of the reorganization energy of the reaction. The possibility of an active site water molecule occupying the empty cavity adjacent to the catalytic base (Asp40) is also addressed. The existence of such a water molecule could explain how the enzyme manages to maintain a low pK(a) for the general base residue.