Metabolomic Studies of Indonesian Jamu Medicines: Prediction of Jamu Efficacy and Identification of Important Metabolites

On Finding Natural Antibiotics based on TCM Formulae

CONTEXT

Novel kinds of antibiotics are needed to combat the emergence of antibacterial resistance. Natural products (NPs) have shown potential as antibiotic candidates. Current experimental methods are not yet capable of exploring the massive, redundant, and noise-involved chemical space of NPs. In silico approaches are needed to select NPs as antibiotic candidates.

OBJECTIVE

This study screens out NPs with antibacterial efficacy guided by both TCM and modern medicine and constructed a dataset aiming to serve the new antibiotic design.

METHOD

A knowledge-based network is proposed in this study involving NPs, herbs, the concepts of TCM, and the treatment protocols (or etiologies) of infectious in modern medicine. Using this network, the NPs candidates are screened out and compose the dataset. Feature selection of machine learning approaches is conducted to evaluate the constructed dataset and statistically validate the im- portance of all NPs candidates for different antibiotics by a classification task.

RESULTS

The extensive experiments prove the constructed dataset reaches a convincing classification performance with a 0.9421 weighted accuracy, 0.9324 recall, and 0.9409 precision. The further visu- alizations of sample importance prove the comprehensive evaluation for model interpretation based on medical value considerations.

Deep Learning Approach for Predicting the Therapeutic Usages of Unani Formulas towards Finding Essential Compounds

The use of herbal medicines in recent decades has increased because their side effects are considered lower than conventional medicine. Unani herbal medicines are often used in Southern Asia. These herbal medicines are usually composed of several types of medicinal plants to treat various diseases. Research on herbal medicine usually focuses on insight into the composition of plants used as ingredients. However, in the present study, we extended to the level of metabolites that exist in the medicinal plants. This study aimed to develop a predictive model of the Unani therapeutic usage based on its constituent metabolites using deep learning and data-intensive science approaches. Furthermore, the best prediction model was then utilized to extract important metabolites for each therapeutic usage of Unani. In this study, it was observed that the deep neural network approach provided a much better prediction model than other algorithms including random forest and support vector machine. Moreover, according to the best prediction model using the deep neural network, we identified 118 important metabolites for nine therapeutic usages of Unani.

Prediction of Potential Natural Antibiotics Plants Based on Jamu Formula Using Random Forest Classifier

Jamu is the traditional Indonesian herbal medicine system that is considered to have many benefits such as serving as a cure for diseases or maintaining sound health. A Jamu medicine is generally made from a mixture of several herbs. Natural antibiotics can provide a way to handle the problem of antibiotic resistance. This research aims to discover the potential of herbal plants as natural antibiotic candidates based on a machine learning approach. Our input data consists of a list of herbal formulas with plants as their constituents. The target class corresponds to bacterial diseases that can be cured by herbal formulas. The best model has been observed by implementing the Random Forest (RF) algorithm. For 10-fold cross-validations, the maximum accuracy, recall, and precision are 91.10%, 91.10%, and 90.54% with standard deviations 1.05, 1.05, and 1.48, respectively, which imply that the model obtained is good and robust. This study has shown that 14 plants can be potentially used as natural antibiotic candidates. Furthermore, according to scientific journals, 10 of the 14 selected plants have direct or indirect antibacterial activity.

Bioactive Components in Traditional Foods Aimed at Health Promotion: A Route to Novel Mechanistic Insights and Lead Molecules?

Traditional foods and diets can provide health benefits beyond their nutrient composition because of the presence of bioactive compounds. In various traditional healthcare systems, diet-based approaches have always played an important role, which has often survived until today. Therefore, investigating traditional foods aimed at health promotion could render not only novel bioactive substances but also mechanistic insights. However, compared to pharmacologically focused research on natural products, investigating such nutrition-based interventions is even more complicated owing to interacting compounds, less potent and relatively subtle effects, the food matrix, and variations in composition and intake. At the same time, technical advances in 'omics' technologies, cheminformatics, and big data analysis create new opportunities, further strengthened by increasing insights into the biology of health and homeostatic resilience. These are to be combined with state-of-the-art ethnobotanical research, which is key to obtaining reliable and reproducible data. Unfortunately, socioeconomic developments and climate change threaten traditional use and knowledge as well as biodiversity. Expected final online publication date for the Annual Review of Food Science and Technology, Volume 13 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Identification of Targeted Proteins by Jamu Formulas for Different Efficacies Using Machine Learning Approach

BACKGROUND

We performed in silico prediction of the interactions between compounds of Jamu herbs and human proteins by utilizing data-intensive science and machine learning methods. Verifying the proteins that are targeted by compounds of natural herbs will be helpful to select natural herb-based drug candidates.

METHODS

Initially, data related to compounds, target proteins, and interactions between them were collected from open access databases. Compounds are represented by molecular fingerprints, whereas amino acid sequences are represented by numerical protein descriptors. Then, prediction models that predict the interactions between compounds and target proteins were constructed using support vector machine and random forest.

RESULTS

A random forest model constructed based on MACCS fingerprint and amino acid composition obtained the highest accuracy. We used the best model to predict target proteins for 94 important Jamu compounds and assessed the results by supporting evidence from published literature and other sources. There are 27 compounds that can be validated by professional doctors, and those compounds belong to seven efficacy groups.

CONCLUSION

By comparing the efficacy of predicted compounds and the relations of the targeted proteins with diseases, we found that some compounds might be considered as drug candidates.

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Biomarker detection is one of the more important biomedical questions for high-throughput 'omics' researchers, and almost all existing biomarker detection algorithms generate one biomarker subset with the optimized performance measurement for a given dataset. However, a recent study demonstrated the existence of multiple biomarker subsets with similarly effective or even identical classification performances. This protocol presents a simple and straightforward methodology for detecting biomarker subsets with binary classification performances, better than a user-defined cutoff. The protocol consists of data preparation and loading, baseline information summarization, parameter tuning, biomarker screening, result visualization and interpretation, biomarker gene annotations, and result and visualization exportation at publication quality. The proposed biomarker screening strategy is intuitive and demonstrates a general rule for developing biomarker detection algorithms. A user-friendly graphical user interface (GUI) was developed using the programming language Python, allowing biomedical researchers to have direct access to their results. The source code and manual of kSolutionVis can be downloaded from http://www.healthinformaticslab.org/supp/resources.php.

Natural occurrence of genotoxic and carcinogenic alkenylbenzenes in Indonesian jamu and evaluation of consumer risks

The consumer risks of jamu, Indonesian traditional herbal medicines, was assessed focussing on the presence of alkenylbenzene containing botanical ingredients. Twenty-three out of 25 samples contained alkenylbenzenes at levels ranging from 3.8 to 440 μg/kg, with methyleugenol being the most frequently encountered alkenylbenzene. The estimated daily intake (EDI) resulting from jamu consumption was estimated to amount to 0.2-171 μg/kg bw/day for individual alkenylbenzenes, to 0.9-203 μg/kg bw/day when adding up all alkenylbenzenes detected, and to 0.9-551 μg/kg bw/day when expressed in methyleugenol equivalents using interim relative potency (REP) factors. The margin of exposure (MOE) values obtained were generally <10,000 indicating a priority for risk management when assuming daily consumption during a lifetime. Using Haber's rule it was estimated that two weeks consumption of these jamu only once would not raise a concern (MOE >10,000). However, when considering use for two weeks every year during a lifetime, 5 samples still raise a concern. It is concluded that the consumption of alkenylbenzene containing jamu can be of concern especially when consumed on a daily basis for longer periods of time on a regular basis.