The multiple orthogonal tools approach to define molecular causation in the validation of druggable targets

Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets

Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.

A Review of Recent Advances and Research on Drug Target Identification Methods

BACKGROUND

From a therapeutic viewpoint, understanding how drugs bind and regulate the functions of their target proteins to protect against disease is crucial. The identification of drug targets plays a significant role in drug discovery and studying the mechanisms of diseases. Therefore the development of methods to identify drug targets has become a popular issue.

METHODS

We systematically review the recent work on identifying drug targets from the view of data and method. We compiled several databases that collect data more comprehensively and introduced several commonly used databases. Then divided the methods into two categories: biological experiments and machine learning, each of which is subdivided into different subclasses and described in detail.

RESULTS

Machine learning algorithms are the majority of new methods. Generally, an optimal set of features is chosen to predict successful new drug targets with similar properties. The most widely used features include sequence properties, network topological features, structural properties, and subcellular locations. Since various machine learning methods exist, improving their performance requires combining a better subset of features and choosing the appropriate model for the various datasets involved.

CONCLUSION

The application of experimental and computational methods in protein drug target identification has become increasingly popular in recent years. Current biological and computational methods still have many limitations due to unbalanced and incomplete datasets or imperfect feature selection methods.

Multi-algorithm and multi-model based drug target prediction and web server

AIM

To develop a reliable computational approach for predicting potential drug targets based merely on protein sequence.

METHODS

With drug target and non-target datasets prepared and 3 classification algorithms (Support Vector Machine, Neural Network and Decision Tree), a multi-algorithm and multi-model based strategy was employed for constructing models to predict potential drug targets.

RESULTS

Twenty one prediction models for each of the 3 algorithms were successfully developed. Our evaluation results showed that ∼30% of human proteins were potential drug targets, and ∼40% of putative targets for the drugs undergoing phase II clinical trials were probably non-targets. A public web server named D3TPredictor (http://www.d3pharma.com/d3tpredictor) was constructed to provide easy access.

CONCLUSION

Reliable and robust drug target prediction based on protein sequences is achieved using the multi-algorithm and multi-model strategy.

New chemical scaffolds for human african trypanosomiasis lead discovery from a screen of tyrosine kinase inhibitor drugs

Human African trypanosomiasis (HAT) is caused by the protozoan Trypanosoma brucei. New drugs are needed to treat HAT because of undesirable side effects and difficulties in the administration of the antiquated drugs that are currently used. In human proliferative diseases, protein tyrosine kinase (PTK) inhibitors (PTKIs) have been developed into drugs (e.g., lapatinib and erlotinib) by optimization of a 4-anilinoquinazoline scaffold. Two sets of facts raise a possibility that drugs targeted against human PTKs could be "hits" for antitrypanosomal lead discoveries. First, trypanosome protein kinases bind some drugs, namely, lapatinib, CI-1033, and AEE788. Second, the pan-PTK inhibitor tyrphostin A47 blocks the endocytosis of transferrin and inhibits trypanosome replication. Following up on these concepts, we performed a focused screen of various PTKI drugs as possible antitrypanosomal hits. Lapatinib, CI-1033, erlotinib, axitinib, sunitinib, PKI-166, and AEE788 inhibited the replication of bloodstream T. brucei, with a 50% growth inhibitory concentration (GI50) between 1.3 μM and 2.5 μM. Imatinib had no effect (i.e., GI50>10 μM). To discover leads among the drugs, a mouse model of HAT was used in a proof-of-concept study. Orally administered lapatinib reduced parasitemia, extended the survival of all treated mice, and cured the trypanosomal infection in 25% of the mice. CI-1033 and AEE788 reduced parasitemia and extended the survival of the infected mice. On the strength of these data and noting their oral bioavailabilities, we propose that the 4-anilinoquinazoline and pyrrolopyrimidine scaffolds of lapatinib, CI-1033, and AEE788 are worth optimizing against T. brucei in medicinal chemistry campaigns (i.e., scaffold repurposing) to discover new drugs against HAT.

A genomic scale map of genetic diversity in Trypanosoma cruzi

BACKGROUND

Trypanosoma cruzi, the causal agent of Chagas Disease, affects more than 16 million people in Latin America. The clinical outcome of the disease results from a complex interplay between environmental factors and the genetic background of both the human host and the parasite. However, knowledge of the genetic diversity of the parasite, is currently limited to a number of highly studied loci. The availability of a number of genomes from different evolutionary lineages of T. cruzi provides an unprecedented opportunity to look at the genetic diversity of the parasite at a genomic scale.

RESULTS

Using a bioinformatic strategy, we have clustered T. cruzi sequence data available in the public domain and obtained multiple sequence alignments in which one or two alleles from the reference CL-Brener were included. These data covers 4 major evolutionary lineages (DTUs): TcI, TcII, TcIII, and the hybrid TcVI. Using these set of alignments we have identified 288,957 high quality single nucleotide polymorphisms and 1,480 indels. In a reduced re-sequencing study we were able to validate ~ 97% of high-quality SNPs identified in 47 loci. Analysis of how these changes affect encoded protein products showed a 0.77 ratio of synonymous to non-synonymous changes in the T. cruzi genome. We observed 113 changes that introduce or remove a stop codon, some causing significant functional changes, and a number of tri-allelic and tetra-allelic SNPs that could be exploited in strain typing assays. Based on an analysis of the observed nucleotide diversity we show that the T. cruzi genome contains a core set of genes that are under apparent purifying selection. Interestingly, orthologs of known druggable targets show statistically significant lower nucleotide diversity values.

CONCLUSIONS

This study provides the first look at the genetic diversity of T. cruzi at a genomic scale. The analysis covers an estimated ~ 60% of the genetic diversity present in the population, providing an essential resource for future studies on the development of new drugs and diagnostics, for Chagas Disease. These data is available through the TcSNP database (http://snps.tcruzi.org).

Testing the promiscuity of commercial kinase inhibitors against the AGC kinase group using a split-luciferase screen

Using a newly developed competitive binding assay dependent upon the reassembly of a split reporter protein, we have tested the promiscuity of a panel of reported kinase inhibitors against the AGC group. Many non-AGC targeted kinase inhibitors target multiple members of the AGC group. In general, structurally similar inhibitors consistently exhibited activity toward the same target as well as toward closely related kinases. The inhibition data was analyzed to test the predictive value of either using identity scores derived from residues within 6 Å of the active site or identity scores derived from the entire kinase domain. The results suggest that the active site identity in certain cases may be a stronger predictor of inhibitor promiscuity. The overall results provide general guidelines for establishing inhibitor selectivity as well as for the future design of inhibitors that either target or avoid AGC kinases.

Species used for drug testing reveal different inhibition susceptibility for 17beta-hydroxysteroid dehydrogenase type 1

Steroid-related cancers can be treated by inhibitors of steroid metabolism. In searching for new inhibitors of human 17beta-hydroxysteroid dehydrogenase type 1 (17β-HSD 1) for the treatment of breast cancer or endometriosis, novel substances based on 15-substituted estrone were validated. We checked the specificity for different 17β-HSD types and species. Compounds were tested for specificity in vitro not only towards recombinant human 17β-HSD types 1, 2, 4, 5 and 7 but also against 17β-HSD 1 of several other species including marmoset, pig, mouse, and rat. The latter are used in the processes of pharmacophore screening. We present the quantification of inhibitor preferences between human and animal models. Profound differences in the susceptibility to inhibition of steroid conversion among all 17β-HSDs analyzed were observed. Especially, the rodent 17β-HSDs 1 were significantly less sensitive to inhibition compared to the human ortholog, while the most similar inhibition pattern to the human 17β-HSD 1 was obtained with the marmoset enzyme. Molecular docking experiments predicted estrone as the most potent inhibitor. The best performing compound in enzymatic assays was also highly ranked by docking scoring for the human enzyme. However, species-specific prediction of inhibitor performance by molecular docking was not possible. We show that experiments with good candidate compounds would out-select them in the rodent model during preclinical optimization steps. Potentially active human-relevant drugs, therefore, would no longer be further developed. Activity and efficacy screens in heterologous species systems must be evaluated with caution.

Lessons for fragment library design: analysis of output from multiple screening campaigns

Over the past 8 years, we have developed, refined and applied a fragment based discovery approach to a range of protein targets. Here we report computational analyses of various aspects of our fragment library and the results obtained for fragment screening. We reinforce the finding of others that the experimentally observed hit rate for screening fragments can be related to a computationally defined druggability index for the target. In general, the physicochemical properties of the fragment hits display the same profile as the library, as is expected for a truly diverse library which probes the relevant chemical space. An analysis of the fragment hits against various protein classes has shown that the physicochemical properties of the fragments are complementary to the properties of the target binding site. The effectiveness of some fragments appears to be achieved by an appropriate mix of pharmacophore features and enhanced aromaticity, with hydrophobic interactions playing an important role. The analysis emphasizes that it is possible to identify small fragments that are specific for different binding sites. To conclude, we discuss how the results could inform further development and improvement of our fragment library.

Searching the Tritryp genomes for drug targets

The recent publication of the complete genome sequences of Leishmania major, Trypanosoma brucei and Trypanosoma cruzi revealed that each genome contains 8300-12,000 protein-coding genes, of which approximately 6500 are common to all three genomes, and ushers in a new, post-genomic, era for trypanosomatid drug discovery. This vast amount of new information makes possible more comprehensive and accurate target identification using several new computational approaches, including identification of metabolic "choke-points", searching the parasite proteomes for orthologues of known drug targets, and identification of parasite proteins likely to interact with known drugs and drug-like small molecules. In this chapter, we describe several databases (such as GENEDB, BRENDA, KEGG, METACYC, the THERAPEUTIC TARGET DATABASE, and CHEMBANK) and algorithms (including PATHOLOGIC, PATHWAY HUNTER TOOL, AND AUToDOCK) which have been developed to facilitate the bioinformatic analyses underlying these approaches. While target identification is only the first step in the drug development pipeline, these new approaches give rise to renewed optimism for the discovery of new drugs to combat the devastating diseases caused by these parasites. Traditionally, drug discovery in the trypanosomatids (and other organisms) has proceeded from two different starting points: screening large numbers of existing compounds for activity against whole parasites or more focused screening of compounds for activity against defined molecular targets. Most existing anti-trypanosomatids drugs were developed using the former approach, although the latter has gained much attention in the last twenty years under the rubric of "rational drug design". Until recently, one of the major bottlenecks in anti-trypanosomatid drug development has been our ability to identify good targets, since only a very small percentage of the total number of trypanosomatid genes were known. That has now changed forever, with the recent (July, 2005) publication of the "Tritryp" (Trypanosoma brucei, Trypanosoma cruzi and Leishmania major) genome sequences. This vast amount of information now makes possible several new approaches for target identification and ushers in a post-genomic era for trypanosomatid drug discovery.

Quantitative analysis on the characteristics of targets with FDA approved drugs

Accumulated knowledge of genomic information, systems biology, and disease mechanisms provide an unprecedented opportunity to elucidate the genetic basis of diseases, and to discover new and novel therapeutic targets from the wealth of genomic data. With hundreds to a few thousand potential targets available in the human genome alone, target selection and validation has become a critical component of drug discovery process. The explorations on quantitative characteristics of the currently explored targets (those without any marketed drug) and successful targets (targeted by at least one marketed drug) could help discern simple rules for selecting a putative successful target. Here we use integrative in silico (computational) approaches to quantitatively analyze the characteristics of 133 targets with FDA approved drugs and 3120 human disease genes (therapeutic targets) not targeted by FDA approved drugs. This is the first attempt to comparatively analyze targets with FDA approved drugs and targets with no FDA approved drug or no drugs available for them. Our results show that proteins with 5 or fewer number of homologs outside their own family, proteins with single-exon gene architecture and proteins interacting with more than 3 partners are more likely to be targetable. These quantitative characteristics could serve as criteria to search for promising targetable disease genes.

Prediction of potential drug targets based on simple sequence properties

Background

During the past decades, research and development in drug discovery have attracted much attention and efforts. However, only 324 drug targets are known for clinical drugs up to now. Identifying potential drug targets is the first step in the process of modern drug discovery for developing novel therapeutic agents. Therefore, the identification and validation of new and effective drug targets are of great value for drug discovery in both academia and pharmaceutical industry. If a protein can be predicted in advance for its potential application as a drug target, the drug discovery process targeting this protein will be greatly speeded up. In the current study, based on the properties of known drug targets, we have developed a sequence-based drug target prediction method for fast identification of novel drug targets.

Results

Based on simple physicochemical properties extracted from protein sequences of known drug targets, several support vector machine models have been constructed in this study. The best model can distinguish currently known drug targets from non drug targets at an accuracy of 84%. Using this model, potential protein drug targets of human origin from Swiss-Prot were predicted, some of which have already attracted much attention as potential drug targets in pharmaceutical research.

Conclusion

We have developed a drug target prediction method based solely on protein sequence information without the knowledge of family/domain annotation, or the protein 3D structure. This method can be applied in novel drug target identification and validation, as well as genome scale drug target predictions.

Neurochemical bases of visceral nociception: mathematical model

A mathematical model of visceral perception was constructed, comprising primary sensory, motor, intestinofugal and principal neurons, interstitial cells of Cajal and smooth muscle elements that are arranged in a functional circuit through chemical synapses. The mathematical description of constructive elements was based on detailed morphological, anatomical, electrophysiological and neuropharmacological characteristics of cells and chemical processes of electrochemical coupling. Emphasis was given to signal transduction mechanisms that involved multiple neurotransmitters and receptor polymodality. The role of co-transmission by acetylcholine (ACh), serotonin (5-HT), noradrenalin (NA), N-methyl-d-aspartate (NMDA) and alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and their corresponding receptors-muscarinic and nicotinic type ACh receptors, beta-adrenoceptors, 5-HT(3/4) type serotonergic receptors, NMDA and AMPA receptors in pathogenesis of nociception was studied numerically. Results of computer simulations reproduced patterns of electrical activity of neurons and mechanical responses of the smooth muscle similar to those observed in in vivo and in vitro experiments when ACh, 5-HT, NA, NMDA and AMPA were acting either alone or co-jointly. The results provide neurochemical bases for explanation of pathophysiological mechanisms of visceral nociception, which cannot be elucidated by existing experimental methods. Care should be taken though when extrapolating the numerical results onto the actual system because of limiting assumptions of the model.

Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness

Identification and validation of viable targets is an important first step in drug discovery and new methods, and integrated approaches are continuously explored to improve the discovery rate and exploration of new drug targets. An in silico machine learning method, support vector machines, has been explored as a new method for predicting druggable proteins from amino acid sequence independent of sequence similarity, thereby facilitating the prediction of druggable proteins that exhibit no or low homology to known targets.

A decade of fragment-based drug design: strategic advances and lessons learned

Since the early 1990s, several technological and scientific advances - such as combinatorial chemistry, high-throughput screening and the sequencing of the human genome - have been heralded as remedies to the problems facing the pharmaceutical industry. The use of these technologies in some form is now well established at most pharmaceutical companies; however, the return on investment in terms of marketed products has not met expectations. Fragment-based drug design is another tool for drug discovery that has emerged in the past decade. Here, we describe the development and evolution of fragment-based drug design, analyse the role that this approach can have in combination with other discovery technologies and highlight the impact that fragment-based methods have made in progressing new medicines into the clinic.

Therapeutic targets: progress of their exploration and investigation of their characteristics

Modern drug discovery is primarily based on the search and subsequent testing of drug candidates acting on a preselected therapeutic target. Progress in genomics, protein structure, proteomics, and disease mechanisms has led to a growing interest in and effort for finding new targets and more effective exploration of existing targets. The number of reported targets of marketed and investigational drugs has significantly increased in the past 8 years. There are 1535 targets collected in the therapeutic target database compared with approximately 500 targets reported in a 1996 review. Knowledge of these targets is helpful for molecular dissection of the mechanism of action of drugs and for predicting features that guide new drug design and the search for new targets. This article summarizes the progress of target exploration and investigates the characteristics of the currently explored targets to analyze their sequence, structure, family representation, pathway association, tissue distribution, and genome location features for finding clues useful for searching for new targets. Possible "rules" to guide the search for druggable proteins and the feasibility of using a statistical learning method for predicting druggable proteins directly from their sequences are discussed.

Predicting protein druggability

The ability to predict whether a particular protein can bind with high affinity and specificity to small, drug-like compounds based solely on its 3D structure has been a longstanding goal of structural biologists and computational scientists. The promise is that an accurate prediction of protein druggability can capitalize on the huge investments already made in structural genomics initiatives by identifying highly druggable proteins and using this information in target identification and validation campaigns. Here we discuss the potential utility of tools that characterize protein targets and describe strategies for the optimal integration of protein druggability data with bioinformatic approaches to target selection.

Human monogenic disorders — a source of novel drug targets

The decrease in new drug applications and approvals over the past several years results from an underlying crisis in drug target identification and validation. Model organisms are being used to address this problem, in combination with novel approaches such as the International HapMap Project. What has been underappreciated is that discovery of new drug targets can also be revived by traditional Mendelian genetics. A large fraction of the human gene repertoire remains phenotypically uncharacterized, and is likely to encode many unanticipated and novel phenotypes that will be of interest to pharmaceutical and biotechnological drug developers.

Chemical genetic approaches to the development of cancer therapeutics

Dysregulation of kinase-based signal transduction networks contributes to multiple aspects of malignancy. Chemical genetic approaches interrogate perturbed signaling in the immediate context of small molecule inhibitor treatment. In recent years, such approaches have identified new kinase targets, clarified the impact of poly-specific inhibition using agents for which at least one primary target is known, and have identified targets for which combinatorial inhibition leads to improved efficacy. Elucidation of the mechanisms through which specific small molecule drug-like agents impact crucial cancer pathways should yield important and clinically translatable insights into the use of similar agents in patients.

RNA interference: from gene silencing to gene-specific therapeutics

In the past 4 years, RNA interference (RNAi) has become widely used as an experimental tool to analyse the function of mammalian genes, both in vitro and in vivo. By harnessing an evolutionary conserved endogenous biological pathway, first identified in plants and lower organisms, double-stranded RNA (dsRNA) reagents are used to bind to and promote the degradation of target RNAs, resulting in knockdown of the expression of specific genes. RNAi can be induced in mammalian cells by the introduction of synthetic double-stranded small interfering RNAs (siRNAs) 21–23 base pairs (bp) in length or by plasmid and viral vector systems that express double-stranded short hairpin RNAs (shRNAs) that are subsequently processed to siRNAs by the cellular machinery. RNAi has been widely used in mammalian cells to define the functional roles of individual genes, particularly in disease. In addition, siRNA and shRNA libraries have been developed to allow the systematic analysis of genes required for disease processes such as cancer using high throughput RNAi screens. RNAi has been used for the knockdown of gene expression in experimental animals, with the development of shRNA systems that allow tissue-specific and inducible knockdown of genes promising to provide a quicker and cheaper way to generate transgenic animals than conventional approaches. Finally, because of the ability of RNAi to silence disease-associated genes in tissue culture and animal models, the development of RNAi-based reagents for clinical applications is gathering pace, as technological enhancements that improve siRNA stability and delivery in vivo, while minimising off-target and nonspecific effects, are developed.

Features of Selective Kinase Inhibitors

Small-molecule inhibitors of protein and lipid kinases have emerged as indispensable tools for studying signal transduction. Despite the widespread use of these reagents, there is little consensus about the biochemical criteria that define their potency and selectivity in cells. We discuss some of the features that determine the cellular activity of kinase inhibitors and propose a framework for interpreting inhibitor selectivity.

The role of natural product chemistry in drug discovery

Although traditionally natural products have played an important role in drug discovery, in the past few years most Big Pharma companies have either terminated or considerably scaled down their natural product operations. This is despite a significant number of natural product-derived drugs being ranked in the top 35 worldwide selling ethical drugs in 2000, 2001, and 2002. There were 15 new natural product-derived drugs launched from 2000 to 2003, as well as 15 natural product-derived compounds in Phase III clinical trials or registration at the end of 2003. Recently, there has been a renewed interest in natural product research due to the failure of alternative drug discovery methods to deliver many lead compounds in key therapeutic areas such as immunosuppression, anti-infectives, and metabolic diseases. To continue to be competitive with other drug discovery methods, natural product research needs to continually improve the speed of the screening, isolation, and structure elucidation processes, as well addressing the suitability of screens for natural product extracts and dealing with issues involved with large-scale compound supply.

The use of nucleic acid tools for target validation in central nervous system therapy

The main challenge facing target validation today comes from the ongoing genomics revolution, which is generating an unprecedented number of potential targets. Existing technologies, such as mouse knockouts, are struggling to provide the throughput now required. Nucleic acid tools including antisense, RNA interference, ribozymes and aptamers offer a potentially higher throughput means of manipulating gene expression and thus validating targets in complex biological systems such as the central nervous system.