Combining label-free cell phenotypic profiling with computational approaches for novel drug discovery

Emerging machine learning approaches to phenotyping cellular motility and morphodynamics

Cells respond heterogeneously to molecular and environmental perturbations. Phenotypic heterogeneity, wherein multiple phenotypes coexist in the same conditions, presents challenges when interpreting the observed heterogeneity. Advances in live cell microscopy allow researchers to acquire an unprecedented amount of live cell image data at high spatiotemporal resolutions. Phenotyping cellular dynamics, however, is a nontrivial task and requires machine learning (ML) approaches to discern phenotypic heterogeneity from live cell images. In recent years, ML has proven instrumental in biomedical research, allowing scientists to implement sophisticated computation in which computers learn and effectively perform specific analyses with minimal human instruction or intervention. In this review, we discuss how ML has been recently employed in the study of cell motility and morphodynamics to identify phenotypes from computer vision analysis. We focus on new approaches to extract and learn meaningful spatiotemporal features from complex live cell images for cellular and subcellular phenotyping.

Prediction of Anti-COVID 19 Therapeutic Power of Medicinal Moroccan Plants Using Molecular Docking

The emerging pathogen SARS-CoV2 causing coronavirus disease 2019 (COVID-19) is a global public health challenge. To the present day, COVID-19 had affected more than 40 million people worldwide. The exploration and the development of new bioactive compounds with cost-effective and specific anti-COVID 19 therapeutic power is the prime focus of the current medical research. Thus, the exploitation of the molecular docking technique has become essential in the discovery and development of new drugs, to better understand drug-target interactions in their original environment. This work consists of studying the binding affinity and the type of interactions, through molecular docking, between 54 compounds from Moroccan medicinal plants, dextran sulfate and heparin (compounds not derived from medicinal plants), and 3CLpro-SARS-CoV-2, ACE2, and the post fusion core of 2019-nCoV S2 subunit. The PDB files of the target proteins and prepared herbal compounds (ligands) were subjected for docking to AutoDock Vina using UCSF Chimera, which provides a list of potential complexes based on the criteria of form complementarity of the natural compound with their binding affinities. The results of molecular docking revealed that Taxol, Rutin, Genkwanine, and Luteolin-glucoside have a high affinity with ACE2 and 3CLpro. Therefore, these natural compounds can have 2 effects at once, inhibiting 3CLpro and preventing recognition between the virus and ACE2. These compounds may have a potential therapeutic effect against SARS-CoV2, and therefore natural anti-COVID-19 compounds.

Computer Aided Drug Design and its Application to the Development of Potential Drugs for Neurodegenerative Disorders

Background

Neurodegenerative disorders (NDs) are diverse group of disorders characterized by escalating loss of neurons (structural and functional). The development of potential therapeutics for NDs presents an important challenge, as traditional treatments are inefficient and usually are unable to stop or retard the process of neurodegeneration. Computer-Aided Drug Design (CADD) has emerged as an efficient means of developing candidate drugs for the treatment of many disease types. Applications of CADD approach to drug discovery are progressing day by day. The recent tendency in drug design is to rationally design potent therapeutics with multi-targeting effects, higher efficacies, and fewer side effects, especially in terms of toxicity.

Methods

A wide literature search was performed for writing this review. An updated view on different types of NDs, their effect on human population and a brief introduction to CADD, various approaches involved in this technique, ranging from structural-based to ligand-based drug design has been discussed. The successful application of CADD approaches for the treatment of neurodegenerative disorders is also included in this review.

Results

In this review, we have briefly described about CADD and its use in the development of the therapeutic drug candidates against NDs. The successful applications, limitations and future prospects of this approach have also been discussed.

Conclusion

CADD can assist researchers studying interactions between drugs and receptors. We believe this review will be helpful for better understanding of CADD and its applications towards the discovery of new drug candidates against various fatal NDs.

An integrated strategy for identifying new targets and inferring the mechanism of action: taking rhein as an example

Background

Target identification is necessary for the comprehensive inference of the mechanism of action of a compound. The application of computational methods to predict the targets of bioactive compounds saves cost and time in drug research and development. Therefore, we designed an integrated strategy consisting of ligand-protein docking, network analysis, enrichment analysis, and an experimental surface plasmon resonance (SPR) method to identify and validate new targets, and then used enriched pathways to elucidate the underlying pharmacological mechanisms. Here, we used rhein, a compound with various pharmacological activities, as an example to find some of its previously unknown targets and to determine its pharmacological activity.

Results

A total of nine candidate targets were discovered, including LCK, HSP90AA1, RAB5A, EGFR, CDK2, CDK6, GSK3B, p38, and JNK. LCK was confirmed through SPR experiments, and HSP90AA1, EGFR, CDK6, p38, and JNK were validated through previous reports. Rhein network regulations are complex and interconnected. The therapeutic effect of rhein is the synergistic and comprehensive result of this vast and complex network, and the perturbation of multiple targets gives rhein its various pharmacological activities.

Conclusions

This study provided a new integrated strategy to identify new targets of bioactive compounds and reveal their molecular mechanisms of action.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2346-4) contains supplementary material, which is available to authorized users.

Label-free technology and patient cells: from early drug development to precision medicine

Drug development requires physiologically more appropriate model systems and assays to increase understanding of drug action and pathological processes in individual humans. Specifically, patient-derived cells offer great opportunities as representative cellular model systems. Moreover, with novel label-free cellular assays, it is often possible to investigate complex biological processes in their native environment. Combining these two offers distinct opportunities for increasing physiological relevance. Here, we review impedance-based label-free technologies in the context of patient samples, focusing on commonly used cell types, including fibroblasts, blood components, and stem cells. Applications extend as far as tissue-on-a-chip models. Thus, applying label-free technologies to patient samples can produce highly biorelevant data and, with them, unique opportunities for drug development and precision medicine.

Systems Pharmacology in Small Molecular Drug Discovery

Drug discovery is a risky, costly and time-consuming process depending on multidisciplinary methods to create safe and effective medicines. Although considerable progress has been made by high-throughput screening methods in drug design, the cost of developing contemporary approved drugs did not match that in the past decade. The major reason is the late-stage clinical failures in Phases II and III because of the complicated interactions between drug-specific, human body and environmental aspects affecting the safety and efficacy of a drug. There is a growing hope that systems-level consideration may provide a new perspective to overcome such current difficulties of drug discovery and development. The systems pharmacology method emerged as a holistic approach and has attracted more and more attention recently. The applications of systems pharmacology not only provide the pharmacodynamic evaluation and target identification of drug molecules, but also give a systems-level of understanding the interaction mechanism between drugs and complex disease. Therefore, the present review is an attempt to introduce how holistic systems pharmacology that integrated in silico ADME/T (i.e., absorption, distribution, metabolism, excretion and toxicity), target fishing and network pharmacology facilitates the discovery of small molecular drugs at the system level.

Compound annotation with real time cellular activity profiles to improve drug discovery

INTRODUCTION

In the past decade, a range of innovative strategies have been developed to improve the productivity of pharmaceutical research and development. In particular, compound annotation, combined with informatics, has provided unprecedented opportunities for drug discovery.

AREAS COVERED

In this review, a literature search from 2000 to 2015 was conducted to provide an overview of the compound annotation approaches currently used in drug discovery. Based on this, a framework related to a compound annotation approach using real-time cellular activity profiles for probe, drug, and biology discovery is proposed.

EXPERT OPINION

Compound annotation with chemical structure, drug-like properties, bioactivities, genome-wide effects, clinical phenotypes, and textural abstracts has received significant attention in early drug discovery. However, these annotations are mostly associated with endpoint results. Advances in assay techniques have made it possible to obtain real-time cellular activity profiles of drug molecules under different phenotypes, so it is possible to generate compound annotation with real-time cellular activity profiles. Combining compound annotation with informatics, such as similarity analysis, presents a good opportunity to improve the rate of discovery of novel drugs and probes, and enhance our understanding of the underlying biology.

Label-Free Cell Phenotypic Identification of D-Luciferin as an Agonist for GPR35

D-Luciferin (also known as beetle or firefly luciferin) is one of the most widely used bioluminescent reporters for monitoring in vitro or in vivo luciferase activity. The identification of several natural phenols and thieno[3,2-b]thiophene-2-carboxylic acid derivatives as agonists for GPR35, an orphan G protein-coupled receptor, had motivated us to examine the pharmacological activity of D-Luciferin, given that it also contains phenol and carboxylic acid moieties. Here, we describe label-free cell phenotypic assays that ascertain D-Luciferin as a partial agonist for GPR35. The agonistic activity of D-Luciferin at the GPR35 shall evoke careful interpretation of biological data when D-Luciferin or its analogues are used as probes.