A systematic identification of multiple toxin–target interactions based on chemical, genomic and toxicological data

Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond

The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.

Systems Pharmacology for Investigation of the Mechanisms of Action of Traditional Chinese Medicine in Drug Discovery

As a traditional medical intervention in Asia and a complementary and alternative medicine in western countries, traditional Chinese medicine (TCM) has attracted global attention in the life science field. TCM provides extensive natural resources for medicinal compounds, and these resources are generally regarded as effective and safe for use in drug discovery. However, owing to the complexity of compounds and their related multiple targets of TCM, it remains difficult to dissect the mechanisms of action of herbal medicines at a holistic level. To solve the issue, in the review, we proposed a novel approach of systems pharmacology to identify the bioactive compounds, predict their related targets, and illustrate the molecular mechanisms of action of TCM. With a predominant focus on the mechanisms of actions of TCM, we also highlighted the application of the systems pharmacology approach for the prediction of drug combination and dynamic analysis, the synergistic effects of TCMs, formula dissection, and theory analysis. In summary, the systems pharmacology method contributes to understand the complex interactions among biological systems, drugs, and complex diseases from a network perspective. Consequently, systems pharmacology provides a novel approach to promote drug discovery in a precise manner and a systems level, thus facilitating the modernization of TCM.

A systems-level approach for investigating organophosphorus pesticide toxicity

The full understanding of the single and joint toxicity of a variety of organophosphorus (OP) pesticides is still unavailable, because of the extreme complex mechanism of action. This study established a systems-level approach based on systems toxicology to investigate OP pesticide toxicity by incorporating ADME/T properties, protein prediction, and network and pathway analysis. The results showed that most OP pesticides are highly toxic according to the ADME/T parameters, and can interact with significant receptor proteins to cooperatively lead to various diseases by the established OP pesticide -protein and protein-disease networks. Furthermore, the studies that multiple OP pesticides potentially act on the same receptor proteins and/or the functionally diverse proteins explained that multiple OP pesticides could mutually enhance toxicological synergy or additive on a molecular/systematic level. To the end, the integrated pathways revealed the mechanism of toxicity of the interaction of OP pesticides and elucidated the pathogenesis induced by OP pesticides. This study demonstrates a systems-level approach for investigating OP pesticide toxicity that can be further applied to risk assessments of various toxins, which is of significant interest to food security and environmental protection.

Systems Pharmacology-based strategy to screen new adjuvant for hepatitis B vaccine from Traditional Chinese Medicine Ophiocordyceps sinensis

Adjuvants are common component for many vaccines but there are still few licensed for human use due to low efficiency or side effects. The present work adopted Systems Pharmacology analysis as a new strategy to screen adjuvants from traditional Chinese medicine. Ophiocordyceps sinensis has been used for many years in China and other Asian countries with many biological properties, but the pharmacological mechanism has not been fully elucidated. First in this study, 190 putative targets for 17 active compounds in Ophiocordyceps sinensis were retrieved and a systems pharmacology-based approach was applied to provide new insights into the pharmacological actions of the drug. Pathway enrichment analysis found that the targets participated in several immunological processes. Based on this, we selected cordycepin as a target compound to serve as an adjuvant of the hepatitis B vaccine because the existing vaccine often fails to induce an effective immune response in many subjects. Animal and cellular experiments finally validated that the new vaccine simultaneously improves the humoral and cellular immunity of BALB/c mice without side effects. All this results demonstrate that cordycepin could work as adjuvant to hepatitis b vaccine and systems-pharmacology analysis could be used as a new method to select adjuvants.

Pred-binding: large-scale protein-ligand binding affinity prediction

Drug target interactions (DTIs) are crucial in pharmacology and drug discovery. Presently, experimental determination of compound-protein interactions remains challenging because of funding investment and difficulties of purifying proteins. In this study, we proposed two in silico models based on support vector machine (SVM) and random forest (RF), using 1589 molecular descriptors and 1080 protein descriptors in 9948 ligand-protein pairs to predict DTIs that were quantified by Ki values. The cross-validation coefficient of determination of 0.6079 for SVM and 0.6267 for RF were obtained, respectively. In addition, the two-dimensional (2D) autocorrelation, topological charge indices and three-dimensional (3D)-MoRSE descriptors of compounds, the autocorrelation descriptors and the amphiphilic pseudo-amino acid composition of protein are found most important for Ki predictions. These models provide a new opportunity for the prediction of ligand-receptor interactions that will facilitate the target discovery and toxicity evaluation in drug development.

Computational Analysis of Structure-Based Interactions for Novel H₁-Antihistamines

As a chronic disorder, insomnia affects approximately 10% of the population at some time during their lives, and its treatment is often challenging. Since the antagonists of the H₁ receptor, a protein prevalent in human central nervous system, have been proven as effective therapeutic agents for treating insomnia, the H₁ receptor is quite possibly a promising target for developing potent anti-insomnia drugs. For the purpose of understanding the structural actors affecting the antagonism potency, presently a theoretical research of molecular interactions between 129 molecules and the H₁ receptor is performed through three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques. The ligand-based comparative molecular similarity indices analysis (CoMSIA) model (Q² = 0.525, R²ncv = 0.891, R²pred = 0.807) has good quality for predicting the bioactivities of new chemicals. The cross-validated result suggests that the developed models have excellent internal and external predictability and consistency. The obtained contour maps were appraised for affinity trends for the investigated compounds, which provides significantly useful information in the rational drug design of novel anti-insomnia agents. Molecular docking was also performed to investigate the mode of interaction between the ligand and the active site of the receptor. Furthermore, as a supplementary tool to study the docking conformation of the antagonists in the H₁ receptor binding pocket, molecular dynamics simulation was also applied, providing insights into the changes in the structure. All of the models and the derived information would, we hope, be of help for developing novel potent histamine H₁ receptor antagonists, as well as exploring the H₁-antihistamines interaction mechanism.

Systems pharmacology-based drug discovery for marine resources: an example using sea cucumber (Holothurians)

ETHNOPHARMACOLOGICAL RELEVANCE

Sea cucumber, a kind of marine animal, have long been utilized as tonic and traditional remedies in the Middle East and Asia because of its effectiveness against hypertension, asthma, rheumatism, cuts and burns, impotence, and constipation. In this study, an overall study performed on sea cucumber was used as an example to show drug discovery from marine resource by using systems pharmacology model. The value of marine natural resources has been extensively considered because these resources can be potentially used to treat and prevent human diseases. However, the discovery of drugs from oceans is difficult, because of complex environments in terms of composition and active mechanisms. Thus, a comprehensive systems approach which could discover active constituents and their targets from marine resource, understand the biological basis for their pharmacological properties is necessary.

MATERIALS AND METHODS

In this study, a feasible pharmacological model based on systems pharmacology was established to investigate marine medicine by incorporating active compound screening, target identification, and network and pathway analysis.

RESULTS

As a result, 106 candidate components of sea cucumber and 26 potential targets were identified. Furthermore, the functions of sea cucumber in health improvement and disease treatment were elucidated in a holistic way based on the established compound-target and target-disease networks, and incorporated pathways.

CONCLUSIONS

This study established a novel strategy that could be used to explore specific active mechanisms and discover new drugs from marine sources.

Profiling the interaction mechanism of indole-based derivatives targeting the HIV-1 gp120 receptor

A glycoprotein exposed on a viral surface, human immunodeficiency virus type 1 (HIV-1) gp120 is essential for virus entry into cells as it plays a vital role in seeking out specific cell surface receptors for entry.

Identifying potential protein targets for toluene using a molecular similarity search, in silico docking and in vitro validation

The toxicity of chemicals greatly depends on their interaction with macromolecular targets.

Insights from systems pharmacology into cardiovascular drug discovery and therapy

Background

Given the complex nature of cardiovascular disease (CVD), information derived from a systems-level will allow us to fully interrogate features of CVD to better understand disease pathogenesis and to identify new drug targets.

Results

Here, we describe a systematic assessment of the multi-layer interactions underlying cardiovascular drugs, targets, genes and disorders to reveal comprehensive insights into cardiovascular systems biology and pharmacology. We have identified 206 effect-mediating drug targets, which are modulated by 254 unique drugs, of which, 43% display activities across different protein families (sequence similarity < 30%), highlighting the fact that multitarget therapy is suitable for CVD. Although there is little overlap between cardiovascular protein targets and disease genes, the two groups have similar pleiotropy and intimate relationships in the human disease gene-gene and cellular networks, supporting their similar characteristics in disease development and response to therapy. We also characterize the relationships between different cardiovascular disorders, which reveal that they share more etiological commonalities with each other rooted in the global disease-disease networks. Furthermore, the disease modular analysis demonstrates apparent molecular connection between 227 cardiovascular disease pairs.

Conclusions

All these provide important consensus as to the cause, prevention, and treatment of various CVD disorders from systems-level perspective.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-014-0141-z) contains supplementary material, which is available to authorized users.

In silico methods to predict drug toxicity

This review describes in silico methods to characterize the toxicity of pharmaceuticals, including tools which predict toxicity endpoints such as genotoxicity or organ-specific models, tools addressing ADME processes, and methods focusing on protein-ligand docking binding. These in silico tools are rapidly evolving. Nowadays, the interest has shifted from classical studies to support toxicity screening of candidates, toward the use of in silico methods to support the expert. These methods, previously considered useful only to provide a rough, initial estimation, currently have attracted interest as they can assist the expert in investigating toxic potential. They provide the expert with safety perspectives and insights within a weight-of-evidence strategy. This represents a shift of the general philosophy of in silico methodology, and it is likely to further evolve especially exploiting links with system biology.