Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties

Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products

Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products.

Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches

Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules.

PECAN Predicts Patterns of Cancer Cell Cytostatic Activity of Natural Products Using Deep Learning

Many machine learning techniques are used as drug discovery tools with the intent to speed characterization by determining relationships between compound structure and biological function. However, particularly in anticancer drug discovery, these models often make only binary decisions about the biological activity for a narrow scope of drug targets. We present a feed-forward neural network, PECAN (Prediction Engine for the Cytostatic Activity of Natural product-like compounds), that simultaneously classifies the potential antiproliferative activity of compounds against 59 cancer cell lines. It predicts the activity to be one of six categories, indicating not only if activity is present but the degree of activity. Using an independent subset of NCI data as a test set, we show that PECAN can reach 60.1% accuracy in a six-way classification and present further evidence that it classifies based on useful structural features of compounds using a "within-one" measure that reaches 93.0% accuracy.

Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products

Natural products (NPs) produced by microorganisms and plants are a major source of drugs, herbicides, and fungicides. Thanks to recent advances in DNA sequencing, bioinformatics, and genome mining tools, a vast amount of data on NP biosynthesis has been generated over the years, which has been increasingly exploited to develop machine learning (ML) tools for NP discovery. In this review, we discuss the latest advances in developing and applying ML tools for exploring the potential NPs that can be encoded by genomic language and predicting the types of bioactivities of NPs. We also examine the technical challenges associated with the development and application of ML tools for NP research.

dbMCS: A Database for Exploring the Mutation Markers of Anti-Cancer Drug Sensitivity

The identification of mutation markers and the selection of appropriate treatment for patients with specific genome mutations are important steps in the development of targeted therapies and the realization of precision medicine for human cancers. To investigate the baseline characteristics of drug sensitivity markers and develop computational methods of mutation effect prediction, we presented a manually curated online- based database of mutation Markers for anti-Cancer drug Sensitivity (dbMCS). Currently, dbMCS contains 1271 mutations and 4427 mutation-disease-drug associations (3151 and 1276 for sensitivity and resistance, respectively) with their PubMed indexed articles. By comparing the mutations in dbMCS with the putative neutral polymorphisms, we investigated the characteristics of drug sensitivity markers. We found that the mutation markers tend to significantly impact on high-conservative regions both in DNA sequences and protein domains. And some of them presented pleiotropic effects depending on the tumor context, appearing concurrently in the sensitivity and resistance categories. In addition, we preliminarily explored the machine learning-based methods for identifying mutation markers of anti-cancer drug sensitivity and produced optimistic results, which suggests that a reliable dataset may provide new insights and essential clues for future cancer pharmacogenomics studies. dbMCS is available at http://bioinfo.aielab.cc/dbMCS/.

Machine learning approaches for elucidating the biological effects of natural products

Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.

Molecular networking as a dereplication strategy for monitoring metabolites of natural product treated cancer cells

RATIONALE

Natural products have been great sources for drug discovery. However, the structures of natural products are diverse and difficult to elucidate. Cordyceps militaris is a parasitic fungus which usually grows on host insects. The metabolites of C. militaris have been reported to act as chemotherapeutic agents. In this study, we aimed for the structural elucidation of specialized metabolites derived from C. militaris, and the metabolic impact in leukemia cells.

METHODS

We describe a liquid chromatography data-dependent mass spectrometric platform combining tandem mass analysis and molecular networking. Leukemia cells treated with C. militaris extract and control groups were visualized in terms of their metabolic profiles using Global Natural Product Social (GNPS) molecular networking. By this method, we were able to elucidate the structures of metabolites from medicinal fungus extracts and cancer cells and then to recognize their changes in a semi-quantitative manner.

RESULTS

Using C. militaris and leukemia cells as examples, we found that approximately 100 new ion species were present in the treated leukemia cells, suggesting a highly altered metabolic profile. Specifically, based on the tandem mass spectral similarity, we proposed that cordycepin, a key fungus-derived therapeutic agent known for its antitumor activity, was transformed into its methylthio form in leukemia cells.

CONCLUSIONS

The platform described provides an ability to investigate complex molecular interactions of natural products in mammalian cells. By incorporating tandem mass spectrometry and molecular networking, we were able to reveal the chemical modification of crude bioactive compounds, for example potential bioactive compounds which might be modified from cordycepin. We envision that such a mass spectrometry (MS)-based workflow, combined with other metabolomics platforms, would enable much wider applicability to cell biology and be of great potential to pharmacological study as well as drug discovery.

Harnessing the Power of Optical Microscopic and Macroscopic Imaging for Natural Products as Cancer Therapeutics

Natural products (NPs) are an important source for new drug discovery over the past decades, which have been demonstrated to be effectively used in cancer prevention, treatment, and adjuvant therapy. Many methods, such as the genomic and metabolomic approaches, immunochemistry, mass spectrometry, and chromatography, have been used to study the effects of NPs on cancer as well as themselves. Because of the advantages in specificity, sensitivity, high throughput, and cost-effectiveness, optical imaging (OI) approaches, including optical microscopic imaging and macroscopic imaging techniques have also been applied in the studies of NPs. Optical microscopic imaging can observe NPs as cancer therapeutics at the cellular level and analyze its cytotoxicity and mechanism of action. Optical macroscopic imaging observes the distribution, metabolic pathway, and target lesions of NPs in vivo, and evaluates NPs as cancer therapeutics at the whole-body level in small living animals. This review focuses on the recent advances in NPs as cancer therapeutics, with particular emphasis on the powerful use of optical microscopic and macroscopic imaging techniques, including the studies of observation of ingestion by cells, anticancer mechanism, and in vivo delivery. Finally, we prospect the wider application and future potential of OI approaches in NPs as cancer therapeutics.

Integration of phytochemicals and phytotherapy into cancer precision medicine

Concepts of individualized therapy in the 1970s and 1980s attempted to develop predictive in vitro tests for individual drug responsiveness without reaching clinical routine. Precision medicine attempts to device novel individual cancer therapy strategies. Using bioinformatics, relevant knowledge is extracted from huge data amounts. However, tumor heterogeneity challenges chemotherapy due to genetically and phenotypically different cell subpopulations, which may lead to refractory tumors. Natural products always served as vital resources for cancer therapy (e.g., Vinca alkaloids, camptothecin, paclitaxel, etc.) and are also sources for novel drugs. Targeted drugs developed to specifically address tumor-related proteins represent the basis of precision medicine. Natural products from plants represent excellent resource for targeted therapies. Phytochemicals and herbal mixtures act multi-specifically, i.e. they attack multiple targets at the same time. Network pharmacology facilitates the identification of the complexity of pharmacogenomic networks and new signaling networks that are distorted in tumors. In the present review, we give a conceptual overview, how the problem of drug resistance may be approached by integrating phytochemicals and phytotherapy into academic western medicine. Modern technology platforms (e.g. “-omics” technologies, DNA/RNA sequencing, and network pharmacology) can be applied for diverse treatment modalities such as cytotoxic and targeted chemotherapy as well as phytochemicals and phytotherapy. Thereby, these technologies represent an integrative momentum to merge the best of two worlds: clinical oncology and traditional medicine. In conclusion, the integration of phytochemicals and phytotherapy into cancer precision medicine represents a valuable asset to chemically synthesized chemicals and therapeutic antibodies.