Identification of Anticancer Enzymes and Biomarkers for Hepatocellular Carcinoma through Constraint-Based Modeling.

Hepatocellular carcinoma (HCC) results in the abnormal regulation of cellular metabolic pathways. Constraint-based modeling approaches can be utilized to dissect metabolic reprogramming, enabling the identification of biomarkers and anticancer targets for diagnosis and treatment. In this study, two genome-scale metabolic models (GSMMs) were reconstructed by employing RNA sequencing expression patterns of hepatocellular carcinoma (HCC) and their healthy counterparts. An anticancer target discovery (ACTD) framework was integrated with the two models to identify HCC targets for anticancer treatment. The ACTD framework encompassed four fuzzy objectives to assess both the suppression of cancer cell growth and the minimization of side effects during treatment. The composition of a nutrient may significantly affect target identification. Within the ACTD framework, ten distinct nutrient media were utilized to assess nutrient uptake for identifying potential anticancer enzymes. The findings revealed the successful identification of target enzymes within the cholesterol biosynthetic pathway using a cholesterol-free cell culture medium. Conversely, target enzymes in the cholesterol biosynthetic pathway were not identified when the nutrient uptake included a cholesterol component. Moreover, the enzymes PGS1 and CRL1 were detected in all ten nutrient media. Additionally, the ACTD framework comprises dual-group representations of target combinations, pairing a single-target enzyme with an additional nutrient uptake reaction. Additionally, the enzymes PGS1 and CRL1 were identified across the ten-nutrient media. Furthermore, the ACTD framework encompasses two-group representations of target combinations involving the pairing of a single-target enzyme with an additional nutrient uptake reaction. Computational analysis unveiled that cell viability for all dual-target combinations exceeded that of their respective single-target enzymes. Consequently, integrating a target enzyme while adjusting an additional exchange reaction could efficiently mitigate cell proliferation rates and ATP production in the treated cancer cells. Nevertheless, most dual-target combinations led to lower side effects in contrast to their single-target counterparts. Additionally, differential expression of metabolites between cancer cells and their healthy counterparts were assessed via parsimonious flux variability analysis employing the GSMMs to pinpoint potential biomarkers. The variabilities of the fluxes and metabolite flow rates in cancer and healthy cells were classified into seven categories. Accordingly, two secretions and thirteen uptakes (including eight essential amino acids and two conditionally essential amino acids) were identified as potential biomarkers. The findings of this study indicated that cancer cells exhibit a higher uptake of amino acids compared with their healthy counterparts.

Reducing State Conflicts between Network Motifs Synergistically Enhances Cancer Drug Effects and Overcomes Adaptive Resistance.

Inducing apoptosis in cancer cells is a primary goal in anti-cancer therapy, but curing cancer with a single drug is unattainable due to drug resistance. The complex molecular network in cancer cells causes heterogeneous responses to single-target drugs, thereby inducing an adaptive drug response. Here, we showed that targeted drug perturbations can trigger state conflicts between multi-stable motifs within a molecular regulatory network, resulting in heterogeneous drug responses. However, we revealed that properly regulating an interconnecting molecule between these motifs can synergistically minimize the heterogeneous responses and overcome drug resistance. We extracted the essential cellular response dynamics of the Boolean network driven by the target node perturbation and developed an algorithm to identify a synergistic combinatorial target that can reduce heterogeneous drug responses. We validated the proposed approach using exemplary network models and a gastric cancer model from a previous study by showing that the targets identified with our algorithm can better drive the networks to desired states than those with other control theories. Of note, our approach suggests a new synergistic pair of control targets that can increase cancer drug efficacy to overcome adaptive drug resistance.

The role of microglia heterogeneity in synaptic plasticity and brain disorders: Will sequencing shed light on the discovery of new therapeutic targets?

Microglia play a crucial role in interacting with neuronal synapses and modulating synaptic plasticity. This function is particularly significant during postnatal development, as microglia are responsible for removing excessive synapses to prevent neurodevelopmental deficits. Dysregulation of microglial synaptic function has been well-documented in various pathological conditions, notably Alzheimer's disease and multiple sclerosis. The recent application of RNA sequencing has provided a powerful and unbiased means to decipher spatial and temporal microglial heterogeneity. By identifying microglia with varying gene expression profiles, researchers have defined multiple subgroups of microglia associated with specific pathological states, including disease-associated microglia, interferon-responsive microglia, proliferating microglia, and inflamed microglia in multiple sclerosis, among others. However, the functional roles of these distinct subgroups remain inadequately characterized. This review aims to refine our current understanding of the potential roles of heterogeneous microglia in regulating synaptic plasticity and their implications for various brain disorders, drawing from recent sequencing research and functional studies. This knowledge may aid in the identification of pathogenetic biomarkers and potential factors contributing to pathogenesis, shedding new light on the discovery of novel drug targets. The field of sequencing-based data mining is evolving toward a multi-omics approach. With advances in viral tools for precise microglial regulation and the development of brain organoid models, we are poised to elucidate the functional roles of microglial subgroups detected through sequencing analysis, ultimately identifying valuable therapeutic targets.

Comprehensive analysis of the role of ubiquitin-specific peptidases in colorectal cancer: A systematic review.

BACKGROUND: Colorectal cancer (CRC) is the third most frequent and the second most fatal cancer. The search for more effective drugs to treat this disease is ongoing. A better understanding of the mechanisms of CRC development and progression may reveal new therapeutic strategies. Ubiquitin-specific peptidases (USPs), the largest group of the deubiquitinase protein family, have long been implicated in various cancers. There have been numerous studies on the role of USPs in CRC; however, a comprehensive view of this role is lacking. AIM: To provide a systematic review of the studies investigating the roles and functions of USPs in CRC. METHODS: We systematically queried the MEDLINE (via PubMed), Scopus, and Web of Science databases. RESULTS: Our study highlights the pivotal role of various USPs in several processes implicated in CRC: Regulation of the cell cycle, apoptosis, cancer stemness, epithelial-mesenchymal transition, metastasis, DNA repair, and drug resistance. The findings of this study suggest that USPs have great potential as drug targets and noninvasive biomarkers in CRC. The dysregulation of USPs in CRC contributes to drug resistance through multiple mechanisms. CONCLUSION: Targeting specific USPs involved in drug resistance pathways could provide a novel therapeutic strategy for overcoming resistance to current treatment regimens in CRC.

Multi-layered genetic approaches to identify approved drug targets.

Drugs targeting genes linked to disease via evidence from human genetics have increased odds of approval. Approaches to prioritize such genes include genome-wide association studies (GWASs), rare variant burden tests in exome sequencing studies (Exome), or integration of a GWAS with expression/protein quantitative trait loci (eQTL/pQTL-GWAS). Here, we compare gene-prioritization approaches on 30 clinically relevant traits and benchmark their ability to recover drug targets. Across traits, prioritized genes were enriched for drug targets with odds ratios (ORs) of 2.17, 2.04, 1.81, and 1.31 for the GWAS, eQTL-GWAS, Exome, and pQTL-GWAS methods, respectively. Adjusting for differences in testable genes and sample sizes, GWAS outperforms e/pQTL-GWAS, but not the Exome approach. Furthermore, performance increased through gene network diffusion, although the node degree, being the best predictor (OR = 8.7), revealed strong bias in literature-curated networks. In conclusion, we systematically assessed strategies to prioritize drug target genes, highlighting the promises and pitfalls of current approaches.

Application of in Silico Technologies for Drug Target Discovery and Pharmacokinetic Analysis.

Drug discovery is researched and developed through many processes, but its overall success rate is extremely low, requiring a very long period of development and considerable costs. Clearly, there is a need to reduce research and development costs by improving the probability of success and increasing process efficiency. One promising approach to this challenge is so-called "in silico drug discovery," which is drug discovery utilizing information and communications technologies (ICT) such as artificial intelligence (AI) and molecular simulation. In recent years, ICT-based science and technology, such as bioinformatics, systems biology, cheminformatics, and molecular simulation, which have been developed mainly in the life science and chemistry fields, have changed the face of drug development. AI-based methods have been developed in the drug discovery process, mainly in relation to drug target discovery and pharmacokinetic analysis. In drug target discovery, an in silico method has been developed that uses a probabilistic framework that eliminates the problems of conventional experimental approaches and provides a key to understanding the pathways and mechanisms from compounds to phenotypes. In the field of pharmacokinetic analysis, we have seen the development of a method using nonclinical data to predict human pharmacokinetic parameters, which are important for predicting drug efficacy and toxicity in clinical trials. In this article, we provide an overview of these methods.

Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy.

The emergence of immunotherapy has dramatically changed the cancer treatment paradigm and generated tremendous promise in precision medicine. However, cancer immunotherapy is greatly limited by its low response rates and immune-related adverse events. Transcriptomics technology is a promising tool for deciphering the molecular underpinnings of immunotherapy response and therapeutic toxicity. In particular, applying single-cell RNA-seq (scRNA-seq) has deepened our understanding of tumor heterogeneity and the microenvironment, providing powerful help for developing new immunotherapy strategies. Artificial intelligence (AI) technology in transcriptome analysis meets the need for efficient handling and robust results. Specifically, it further extends the application scope of transcriptomic technologies in cancer research. AI-assisted transcriptomic analysis has performed well in exploring the underlying mechanisms of drug resistance and immunotherapy toxicity and predicting therapeutic response, with profound significance in cancer treatment. In this review, we summarized emerging AI-assisted transcriptomic technologies. We then highlighted new insights into cancer immunotherapy based on AI-assisted transcriptomic analysis, focusing on tumor heterogeneity, the tumor microenvironment, immune-related adverse event pathogenesis, drug resistance, and new target discovery. This review summarizes solid evidence for immunotherapy research, which might help the cancer research community overcome the challenges faced by immunotherapy.

Profiling disease-selective drug targets: From proteomics to ligandomics.

Despite advancements in omics technologies, including proteomics and transcriptomics, identification of therapeutic targets remains challenging. Ligandomics recently emerged as a unique technology of functional proteomics for global profiling of cell-binding protein ligands. When applied to diseased versus healthy vasculatures, comparative ligandomics systematically maps novel disease-restricted ligands that allow selective targeting of pathological but not physiological pathways, providing high efficacy with intrinsic safety. In this review, we discuss the potential of cellular ligands as therapeutic targets and summarize the development of ligandomics. We further compare the advantages and limitations of different omics technologies for drug target discovery and discuss target selection criteria to improve drug R&D success rates.

Identification of a novel ALDH1A3-selective inhibitor by a chemical probe with unrelated bioactivity: An approach to affinity-based drug target discovery.

The identification of biologically active target compounds and their binding proteins is important in mechanism-of-action studies for drug development. Additionally, the newly discovered binding proteins provide unforeseen ideas for novel drug discovery and for subsequent structural transformation to improve target specificity. Based on the lead and final candidate compounds related to the type 5 phosphodiesterase (PDE5) inhibitor E4021, we designed chemical probes and identified their target proteins by the affinity chromatography approach. Aldehyde dehydrogenase family 1 member A3 (ALDH1A3), currently reported as a cancer stem cell target, was clearly isolated as a binding protein of the lead 'immature' inhibitor probe against PDE5. In the early derivatization to the closely related structure, Compound 5 (ER-001135935) was found to significantly inhibit ALDH1A3 activity. The discovery process of a novel ALDH1A3-selective inhibitor with affinity-based binder identification is described, and the impact of this identification method on novel drug discovery is discussed.

A review of biomedical datasets relating to drug discovery: a knowledge graph perspective.

Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritization. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, while relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data are required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorized according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and an evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, while also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.

Implications of topological imbalance for representation learning on biomedical knowledge graphs.

Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KGs) that utilize the interconnected nature of the domain. Graph-based modelling of the data, combined with KG embedding (KGE) methods, are promising as they provide a more intuitive representation and are suitable for inference tasks such as predicting missing links. One common application is to produce ranked lists of genes for a given disease, where the rank is based on the perceived likelihood of association between the gene and the disease. It is thus critical that these predictions are not only pertinent but also biologically meaningful. However, KGs can be biased either directly due to the underlying data sources that are integrated or due to modelling choices in the construction of the graph, one consequence of which is that certain entities can get topologically overrepresented. We demonstrate the effect of these inherent structural imbalances, resulting in densely connected entities being highly ranked no matter the context. We provide support for this observation across different datasets, models as well as predictive tasks. Further, we present various graph perturbation experiments which yield more support to the observation that KGE models can be more influenced by the frequency of entities rather than any biological information encoded within the relations. Our results highlight the importance of data modelling choices, and emphasizes the need for practitioners to be mindful of these issues when interpreting model outputs and during KG composition.

FL-DTD: an integrated pipeline to predict the drug interacting targets by feedback loop-based network analysis.

Drug target discovery is an essential step to reveal the mechanism of action (MoA) underlying drug therapeutic effects and/or side effects. Most of the approaches are usually labor-intensive while unable to identify the tissue-specific interacting targets, especially the targets with weaker drug binding affinity. In this work, we proposed an integrated pipeline, FL-DTD, to predict the drug interacting targets of novel compounds in a tissue-specific manner. This method was built based on a hypothesis that cells under a status of homeostasis would take responses to drug perturbation by activating feedback loops. Therefore, the drug interacting targets can be predicted by analyzing the network responses after drug perturbation. We evaluated this method using the expression data of estrogen stimulation, gene manipulation and drug perturbation and validated its good performance to identify the annotated drug targets. Using STAT3 as a target protein, we applied this method to drug perturbation data of 500 natural compounds and predicted five compounds with STAT3 interacting activities. Experimental assay validated the STAT3-interacting activities of four compounds. Overall, our evaluation suggests that FL-DTD predicts the drug interacting targets with good accuracy and can be used for drug target discovery.

A rapid CRISPR competitive assay for in vitro and in vivo discovery of potential drug targets affecting the hematopoietic system.

CRISPR/Cas9 can be used as an experimental tool to inactivate genes in cells. However, a CRISPR-targeted cell population will not show a uniform genotype of the targeted gene. Instead, a mix of genotypes is generated - from wild type to different forms of insertions and deletions. Such mixed genotypes complicate analysis of the role of the targeted gene in the studied cell population. Here, we present a rapid and universal experimental approach to functionally analyze a CRISPR-targeted cell population that does not involve generating clonal lines. As a simple readout, we leverage the CRISPR-induced genetic heterogeneity and use sequencing to identify how different genotypes are enriched or depleted in relation to the studied cellular behavior or phenotype. The approach uses standard PCR, Sanger sequencing, and a simple sequence deconvoluting software, enabling laboratories without specific in-depth experience to perform these experiments. As proof of principle, we present examples studying various aspects related to hematopoietic cells (T cell development in vivo and activation in vitro, differentiation of macrophages and dendritic cells, as well as a leukemia-like phenotype induced by overexpressing a proto-oncogene). In conclusion, we present a rapid experimental approach to identify potential drug targets related to mature immune cells, as well as normal and malignant hematopoiesis.

Comparative microsomal proteomics of a model lung cancer cell line NCI-H23 reveals distinct differences between molecular profiles of 3D and 2D cultured cells.

Lung cancer is the leading cause of cancer-related deaths in the USA and worldwide. Yet, about 95% of new drug candidates validated in preclinical phase eventually fail in clinical trials. Such a high attrition rate is attributed mostly to the inability of conventional two-dimensionally (2D) cultured cancer cells to mimic native three-dimensional (3D) growth of malignant cells in human tumors. To ascertain phenotypical differences between these two distinct culture conditions, we carried out a comparative proteomic analysis of a membrane fraction obtained from 3D- and 2D-cultured NSCLC model cell line NCI-H23. This analysis revealed a map of 1,166 (24%) protein species regulated in culture dependent manner, including differential regulation of a subset of cell surface-based CD molecules. We confirmed exclusive expression of CD99, CD146 and CD239 in 3D culture. Furthermore, label-free quantitation, targeting KRas proteoform-specific peptides, revealed upregulation of both wild type and monoallelic KRas4BG12C mutant at the surface of 3D cultured cells. In order to reduce the high attrition rate of new drug candidates, the results of this study strongly suggests exploiting base-line molecular profiling of a large number of patient-derived NSCLC cell lines grown in 2D and 3D culture, prior to actual drug candidate testing.

Proteo-transcriptomics meta-analysis identifies SUMO2 as a promising target in glioblastoma multiforme therapeutics.

BACKGROUND: Glioblastoma multiforme (GBM) is a deadly brain tumour with minimal survival rates due to the ever-expanding heterogeneity, chemo and radioresistance. Kinases are known to crucially drive GBM pathology; however, a rationale therapeutic combination that can simultaneously inhibit multiple kinases has not yet emerged successfully. RESULTS: Here, we analyzed the GBM patient data from several publicly available repositories and deduced hub GBM kinases, most of which were identified to be SUMOylated by SUMO2/3 isoforms. Not only the hub kinases but a significant proportion of GBM upregulated genes involved in proliferation, metastasis, invasion, epithelial-mesenchymal transition, stemness, DNA repair, stromal and macrophages maintenance were also identified to be the targets of SUMO2 isoform. Correlatively, high expression of SUMO2 isoform was found to be significantly associated with poor patient survival. CONCLUSIONS: Although many natural products and drugs are evidenced to target general SUMOylation, however, our meta-analysis strongly calls for the need to design SUMO2/3 or even better SUMO2 specific inhibitors and also explore the SUMO2 transcription inhibitors for universally potential, physiologically non-toxic anti-GBM drug therapy.

Exploring Candidate Human Synthetic Lethal Interactions Through siRNA and Quantitative Imaging-Based Approaches.

Synthetic lethal interactions can assist in characterizing protein functions and cellular processes, but they can also be used to identify novel drug targets for the development of innovative cancer therapeutic strategies. Despite recent technological advancements including CRISPR/Cas9 approaches, the systematic assessment of all pairwise gene interactions in humans (~ 200 million pairs) remains an unmet goal. Thus, hypothesis-driven approaches, which prioritize subsets of promising candidate SL interactions for experimental assessment, are critical to expedite the identification of novel SL interactions. Here, we provide a guide to screen and validate focused libraries of promising candidate SL interactions, typically consisting of 50-500 targets. First, we describe two siRNA and image-based screening protocols to rapidly assess candidate SL interactions. Subsequently, we provide methods to validate a subset of the most promising interactions uncovered in the screens. These approaches employ commercially available reagents and standard laboratory equipment to facilitate and expedite the identification of bona fide human SL interactions.

Drug target discovery by magnetic nanoparticles coupled mass spectrometry.

Drug target discovery is the basis of drug screening. It elucidates the cause of disease and the mechanism of drug action, which is the essential of drug innovation. Target discovery performed in biological systems is complicated as proteins are in low abundance and endogenous compounds may interfere with drug binding. Therefore, methods to track drug-target interactions in biological matrices are urgently required. In this work, a Fe3O4 nanoparticle-based approach was developed for drug-target screening in biofluids. A known ligand-protein complex was selected as a principle-to-proof example to validate the feasibility. After incubation in cell lysates, ligand-modified Fe3O4 nanoparticles bound to the target protein and formed complexes that were separated from the lysates by a magnet for further analysis. The large surface-to-volume ratio of the nanoparticles provides more active sites for the modification of chemical drugs. It enhances the opportunity for ligand-protein interactions, which is beneficial for capturing target proteins, especially for those with low abundance. Additionally, a one-step magnetic separation simplifies the pre-processing of ligand-protein complexes, so it effectively reduces the endogenous interference. Therefore, the present nanoparticle-based approach has the potential to be used for drug target screening in biological systems.

Path to Clonal Theranostics in Luminal Breast Cancers.

Integrating tumor heterogeneity in the drug discovery process is a key challenge to tackle breast cancer resistance. Identifying protein targets for functionally distinct tumor clones is particularly important to tailor therapy to the heterogeneous tumor subpopulations and achieve clonal theranostics. For this purpose, we performed an unsupervised, label-free, spatially resolved shotgun proteomics guided by MALDI mass spectrometry imaging (MSI) on 124 selected tumor clonal areas from early luminal breast cancers, tumor stroma, and breast cancer metastases. 2868 proteins were identified. The main protein classes found in the clonal proteome dataset were enzymes, cytoskeletal proteins, membrane-traffic, translational or scaffold proteins, or transporters. As a comparison, gene-specific transcriptional regulators, chromatin related proteins or transmembrane signal receptor were more abundant in the TCGA dataset. Moreover, 26 mutated proteins have been identified. Similarly, expanding the search to alternative proteins databases retrieved 126 alternative proteins in the clonal proteome dataset. Most of these alternative proteins were coded mainly from non-coding RNA. To fully understand the molecular information brought by our approach and its relevance to drug target discovery, the clonal proteomic dataset was further compared to the TCGA breast cancer database and two transcriptomic panels, BC360 (nanoString®) and CDx (Foundation One®). We retrieved 139 pathways in the clonal proteome dataset. Only 55% of these pathways were also present in the TCGA dataset, 68% in BC360 and 50% in CDx. Seven of these pathways have been suggested as candidate for drug targeting, 22 have been associated with breast cancer in experimental or clinical reports, the remaining 19 pathways have been understudied in breast cancer. Among the anticancer drugs, 35 drugs matched uniquely with the clonal proteome dataset, with only 7 of them already approved in breast cancer. The number of target and drug interactions with non-anticancer drugs (such as agents targeting the cardiovascular system, metabolism, the musculoskeletal or the nervous systems) was higher in the clonal proteome dataset (540 interactions) compared to TCGA (83 interactions), BC360 (419 interactions), or CDx (172 interactions). Many of the protein targets identified and drugs screened were clinically relevant to breast cancer and are in clinical trials. Thus, we described the non-redundant knowledge brought by this clone-tailored approach compared to TCGA or transcriptomic panels, the targetable proteins identified in the clonal proteome dataset, and the potential of this approach for drug discovery and repurposing through drug interactions with antineoplastic agents and non-anticancer drugs.

Drug target discovery by magnetic nanoparticles coupled mass spectrometry / 药物分析学报

Drug target discovery is the basis of drug screening.It elucidates the cause of disease and the mechanism of drug action,which is the essential of drug innovation.Target discovery performed in biological sys-tems is complicated as proteins are in low abundance and endogenous compounds may interfere with drug binding.Therefore,methods to track drug-target interactions in biological matrices are urgently required.In this work,a Fe3O4 nanoparticle-based approach was developed for drug-target screening in biofluids.A known ligand-protein complex was selected as a principle-to-proof example to validate the feasibility.After incubation in cell lysates,ligand-modified Fe3O4 nanoparticles bound to the target protein and formed complexes that were separated from the lysates by a magnet for further analysis.The large surface-to-volume ratio of the nanoparticles provides more active sites for the modification of chemical drugs.It enhances the opportunity for ligand-protein interactions,which is beneficial for capturing target proteins,especially for those with low abundance.Additionally,a one-step magnetic separation simplifies the pre-processing of ligand-protein complexes,so it effectively reduces the endogenous interference.Therefore,the present nanoparticle-based approach has the potential to be used for drug target screening in biological systems.

Chemoproteomics: Towards Global Drug Target Profiling.

Illuminating drugs' mechanisms of action and their effects on the biomolecules of pathogens and humans is a much-needed next step to facilitate pharmaceutical development. Although studies have linked some drugs to their therapeutic targets by using transcriptomics and genomics, these approaches have intrinsic limitations and cannot directly assess drugs' effects on their protein targets. In this regard, chemoproteomic methods can detect protein-ligand interactions and quantitate the chemical or thermal stability changes of the entire detectible proteomes induced by drugs of interest. These widely applicable techniques have recently been adapted to deconvolute the mechanisms of action of antiparasitic drugs and successfully identified an essential target that was previously not known to be druggable. A continued effort to Integrate chemoproteomics into the drug-development pipeline could greatly improve our understanding of drugs' mechanisms, toxicity and pharmacodynamic properties.