Requirements for a lead compound to become a clinical candidate

A drug candidate suitable for clinical testing is expected to bind selectively to the receptor site on the target, to elicit the desired functional response of the target molecule, and to have adequate bioavailability and biodistribution to elicit the desired responses in animals and humans; it must also pass formal toxicity evaluation in animals. The path from lead to clinical drug candidate represents the most idiosyncratic segment of drug discovery and development. Each program is unique and setbacks are common, making it difficult to predict accurately the duration or costs of this segment. Because of incidents of unpredicted human toxicity seen in recent years, the regulatory agencies and public demands for safety of new drug candidates have become very strict, and safety issues are dominant when identifying a clinical drug candidate

Phenotypic Screens, Chemical Genomics, and Antimalarial Lead Discovery

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Evaluation of a Bayesian inference network for ligand-based virtual screening

Background Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query. Here, we modify the approach to enable a Bayesian inference network to be used for chemical similarity searching, where a database is ranked in order of decreasing probability of bioactivity. Results Bayesian inference networks were implemented using two different types of network and four different types of belief function. Experiments with the MDDR and WOMBAT databases show that a Bayesian inference network can be used to provide effective ligand-based screening, especially when the active molecules being sought have a high degree of structural homogeneity; in such cases, the network substantially out-performs a conventional, Tanimoto-based similarity searching system. However, the effectiveness of the network is much less when structurally heterogeneous sets of actives are being sought. Conclusion A Bayesian inference network provides an interesting alternative to existing tools for ligand-based virtual screening

Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed

Core Site-Moiety Maps Reveal Inhibitors and Binding Mechanisms of Orthologous Proteins by Screening Compound Libraries

Members of protein families often share conserved structural subsites for interaction with chemically similar moieties despite low sequence identity. We propose a core site-moiety map of multiple proteins (called CoreSiMMap) to discover inhibitors and mechanisms by profiling subsite-moiety interactions of immense screening compounds. The consensus anchor, the subsite-moiety interactions with statistical significance, of a CoreSiMMap can be regarded as a “hot spot” that represents the conserved binding environments involved in biological functions. Here, we derive the CoreSiMMap with six consensus anchors and identify six inhibitors (IC50<8.0 µM) of shikimate kinases (SKs) of Mycobacterium tuberculosis and Helicobacter pylori from the NCI database (236,962 compounds). Studies of site-directed mutagenesis and analogues reveal that these conserved interacting residues and moieties contribute to pocket-moiety interaction spots and biological functions. These results reveal that our multi-target screening strategy and the CoreSiMMap can increase the accuracy of screening in the identification of novel inhibitors and subsite-moiety environments for elucidating the binding mechanisms of targets

Mycobacterial dihydrofolate reductase inhibitors identified using chemogenomic methods and in vitro validation.

The lack of success in target-based screening approaches to the discovery of antibacterial agents has led to reemergence of phenotypic screening as a successful approach of identifying bioactive, antibacterial compounds. A challenge though with this route is then to identify the molecular target(s) and mechanism of action of the hits. This target identification, or deorphanization step, is often essential in further optimization and validation studies. Direct experimental identification of the molecular target of a screening hit is often complex, precisely because the properties and specificity of the hit are not yet optimized against that target, and so many false positives are often obtained. An alternative is to use computational, predictive, approaches to hypothesize a mechanism of action, which can then be validated in a more directed and efficient manner. Specifically here we present experimental validation of an in silico prediction from a large-scale screen performed against Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis. The two potent anti-tubercular compounds studied in this case, belonging to the tetrahydro-1,3,5-triazin-2-amine (THT) family, were predicted and confirmed to be an inhibitor of dihydrofolate reductase (DHFR), a known essential Mtb gene, and already clinically validated as a drug target. Given the large number of similar screening data sets shared amongst the community, this in vitro validation of these target predictions gives weight to computational approaches to establish the mechanism of action (MoA) of novel screening hit

New Polynomial-Based Molecular Descriptors with Low Degeneracy

In this paper, we introduce a novel graph polynomial called the ‘information polynomial’ of a graph. This graph polynomial can be derived by using a probability distribution of the vertex set. By using the zeros of the obtained polynomial, we additionally define some novel spectral descriptors. Compared with those based on computing the ordinary characteristic polynomial of a graph, we perform a numerical study using real chemical databases. We obtain that the novel descriptors do have a high discrimination power

Advancing Drug Innovation for Neglected Diseases—Criteria for Lead Progression

The current drug R&D pipeline for most neglected diseases remains weak, and unlikely to support registration of novel drug classes that meet desired target product profiles in the short term. This calls for sustained investment as well as greater emphasis in the risky upstream drug discovery. Access to technologies, resources, and strong management as well as clear compound progression criteria are factors in the successful implementation of any collaborative drug discovery effort. We discuss how some of these factors have impacted drug discovery for tropical diseases within the past four decades, and highlight new opportunities and challenges through the virtual North–South drug discovery network as well as the rationale for greater participation of institutions in developing countries in product innovation. A set of criteria designed to facilitate compound progression from screening hits to drug candidate selection is presented to guide ongoing efforts

Soluble CD44 Interacts with Intermediate Filament Protein Vimentin on Endothelial Cell Surface

CD44 is a cell surface glycoprotein that functions as hyaluronan receptor. Mouse and human serum contain substantial amounts of soluble CD44, generated either by shedding or alternative splicing. During inflammation and in cancer patients serum levels of soluble CD44 are significantly increased. Experimentally, soluble CD44 overexpression blocks cancer cell adhesion to HA. We have previously found that recombinant CD44 hyaluronan binding domain (CD44HABD) and its non-HA-binding mutant inhibited tumor xenograft growth, angiogenesis, and endothelial cell proliferation. These data suggested an additional target other than HA for CD44HABD. By using non-HA-binding CD44HABD Arg41Ala, Arg78Ser, and Tyr79Ser-triple mutant (CD443MUT) we have identified intermediate filament protein vimentin as a novel interaction partner of CD44. We found that vimentin is expressed on the cell surface of human umbilical vein endothelial cells (HUVEC). Endogenous CD44 and vimentin coprecipitate from HUVECs, and when overexpressed in vimentin-negative MCF-7 cells. By using deletion mutants, we found that CD44HABD and CD443MUT bind vimentin N-terminal head domain. CD443MUT binds vimentin in solution with a Kd in range of 12–37 nM, and immobilised vimentin with Kd of 74 nM. CD443MUT binds to HUVEC and recombinant vimentin displaces CD443MUT from its binding sites. CD44HABD and CD443MUT were internalized by wild-type endothelial cells, but not by lung endothelial cells isolated from vimentin knock-out mice. Together, these data suggest that vimentin provides a specific binding site for soluble CD44 on endothelial cells