Classical vs reverse pharmacology in drug discovery

Drug-Drug Interactions and Synergy: From Pharmacological Models to Clinical Application

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts, such as concentration-response curves, additive effects, and predictive models, are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. Although various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors, such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care. SIGNIFICANCE STATEMENT: Combining drugs with different mechanisms of action for synergistic interactions optimizes treatment efficacy and safety. Accurate interpretation of synergy requires the identification of the expected additive effect. Despite innovative models to predict the additive effect, consensus in drug-drug interactions research is lacking, hindering the bench-to-bedside development of combination therapies. Collaboration among science, industry, and regulation is crucial for advancing combination therapy development, ensuring rigorous application of predictive models in clinical settings.

Directional ΔG Neural Network (DrΔG-Net): A Modular Neural Network Approach to Binding Free Energy Prediction

The protein-ligand binding free energy is a central quantity in structure-based computational drug discovery efforts. Although popular alchemical methods provide sound statistical means of computing the binding free energy of a large breadth of systems, they are generally too costly to be applied at the same frequency as end point or ligand-based methods. By contrast, these data-driven approaches are typically fast enough to address thousands of systems but with reduced transferability to unseen systems. We introduce DrΔG-Net (or simply Dragnet), an equivariant graph neural network that can blend ligand-based and protein-ligand data-driven approaches. It is based on a 3D fingerprint representation of the ligand alone and in complex with the protein target. Dragnet is a global scoring function to predict the binding affinity of arbitrary protein-ligand complexes, but can be easily tuned via transfer learning to specific systems or end points, performing similarly to common 2D ligand-based approaches in these tasks. Dragnet is evaluated on a total of 28 validation proteins with a set of congeneric ligands derived from the Binding DB and one custom set extracted from the ChEMBL Database. In general, a handful of experimental binding affinities are sufficient to optimize the scoring function for a particular protein and ligand scaffold. When not available, predictions from physics-based methods such as absolute free energy perturbation can be used for the transfer learning tuning of Dragnet. Furthermore, we use our data to illustrate the present limitations of data-driven modeling of binding free energy predictions.

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks

Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have greatly benefited large-scale predictions of protein interactions using machine learning methods. A wide range of tools have been developed to predict protein-protein, protein-nucleic acid, and protein-drug interactions. Here, we discuss the applications, methods, and challenges faced when employing the various prediction methods. We also briefly describe ways to overcome the challenges and prospective future developments in the field of protein interaction biology.

Medicinal Plant Preparations Administered by Botswana Traditional Health Practitioners for Treatment of Worm Infections Show Anthelmintic Activities

Schistosomiasis and soil-transmitted helminths are some of the priority neglected tropical diseases (NTDs) targeted for elimination by the World Health Organization (WHO). They are prevalent in Botswana and although Botswana has begun mass drug administration with the hope of eliminating soil-transmitted helminths as a public health problem, the prevalence of schistosomiasis does not meet the threshold required to warrant large-scale interventions. Although Botswana has a modern healthcare system, many people in Botswana rely on traditional medicine to treat worm infections and schistosomiasis. In this study, ten plant species used by traditional health practitioners against worm infections were collected and tested against Ancylostoma ceylanicum (zoonotic hookworm), Heligmosomoides polygyrus (roundworm of rodents), Necator americanus (New World hookworm), Schistosoma mansoni (blood fluke) [adult and newly transformed schistosomula (NTS)], Strongyloides ratti (threadworm) and Trichuris muris (nematode parasite of mice) in vitro. Extracts of two plants, Laphangium luteoalbum and Commiphora pyaracanthoides, displayed promising anthelmintic activity against NTS and adult S. mansoni, respectively. L. luteoalbum displayed 85.4% activity at 1 μg/mL against NTS, while C. pyracanthoides displayed 78.5% activity against adult S. mansoni at 10 μg/mL.

Recent Advances in Cancer Drug Discovery Through the Use of Phenotypic Reporter Systems, Connectivity Mapping, and Pooled CRISPR Screening

Multi-omic approaches offer an unprecedented overview of the development, plasticity, and resistance of cancer. However, the translation from anti-cancer compounds identified in vitro to clinically active drugs have a notoriously low success rate. Here, we review how technical advances in cell culture, robotics, computational biology, and development of reporter systems have transformed drug discovery, enabling screening approaches tailored to clinically relevant functional readouts (e.g., bypassing drug resistance). Illustrating with selected examples of “success stories,” we describe the process of phenotype-based high-throughput drug screening to target malignant cells or the immune system. Second, we describe computational approaches that link transcriptomic profiling of cancers with existing pharmaceutical compounds to accelerate drug repurposing. Finally, we review how CRISPR-based screening can be applied for the discovery of mechanisms of drug resistance and sensitization. Overall, we explore how the complementary strengths of each of these approaches allow them to transform the paradigm of pre-clinical drug development.

The Discovery of New Drug-Target Interactions for Breast Cancer Treatment

Drug–target interaction (DTIs) prediction plays a vital role in probing new targets for breast cancer research. Considering the multifaceted challenges associated with experimental methods identifying DTIs, the in silico prediction of such interactions merits exploration. In this study, we develop a feature-based method to infer unknown DTIs, called PsePDC-DTIs, which fuses information regarding protein sequences extracted by pseudo-position specific scoring matrix (PsePSSM), detrended cross-correlation analysis coefficient (DCCA coefficient), and an FP2 format molecular fingerprint descriptor of drug compounds. In addition, the synthetic minority oversampling technique (SMOTE) is employed for dealing with the imbalanced data after Lasso dimensionality reduction. Then, the processed feature vectors are put into a random forest classifier to perform DTIs predictions on four gold standard datasets, including nuclear receptors (NR), G-protein-coupled receptors (GPCR), ion channels (IC), and enzymes (E). Furthermore, we explore new targets for breast cancer treatment using its risk genes identified from large-scale genome-wide genetic studies using PsePDC-DTIs. Through five-fold cross-validation, the average values of accuracy in NR, GPCR, IC, and E datasets are 95.28%, 96.19%, 96.74%, and 98.22%, respectively. The PsePDC-DTIs model provides us with 10 potential DTIs for breast cancer treatment, among which erlotinib (DB00530) and FGFR2 (hsa2263), caffeine (DB00201) and KCNN4 (hsa3783), as well as afatinib (DB08916) and FGFR2 (hsa2263) are found with direct or inferred evidence. The PsePDC-DTIs model has achieved good prediction results, establishing the validity and superiority of the proposed method.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

Prophylactic strategies to control chikungunya virus infection

Chikungunya virus (CHIKV) is a (re)emerging arbovirus and the causative agent of chikungunya fever. In recent years, CHIKV was responsible for a series of outbreaks, some of which had serious economic and public health impacts in the affected regions. So far, no CHIKV-specific antiviral therapy or vaccine has been approved. This review gives a brief summary on CHIKV epidemiology, spread, infection and diagnosis. It furthermore deals with the strategies against emerging diseases, drug development and the possibilities of testing antivirals against CHIKV in vitro and in vivo. With our review, we hope to provide the latest information on CHIKV, disease manifestation, as well as on the current state of CHIKV vaccine development and post-exposure therapy.

A comprehensive screening method for investigating the potential binding targets of doxorubicin based on protein microarray

With the development of precision therapy, pharmacological research pays more and more attention to seek and confirm the target of drugs in order to understand the mechanism of drug action and reduce side effects. Screening candidate proteins can be effectively used to predict potential drug targets and toxicity. Therefore, a high-throughput drug-binding protein screening method based on protein microarray which contains over 21,000 human proteins was introduced in this investigation. Doxorubicin, a classical chemotherapeutic agent widely used in clinical treatment, was taken as a drug example in our protein screening study. Through microarray and bioinformatics analysis, more potential targets were found with different binding affinity to doxorubicin, and HRAS stands out as a critical protein from candidate proteins. In addition, the results revealed that the formation of the HRAS-RAF complex is promoted by doxorubicin. It is our expectation that the outcomes could benefit to understand the various effect of the doxorubicin and push the protein microarray screening to apply in the comprehensive pharmacological and toxicological investigation of other drugs.

The Role of Norrish Type-I Chemistry in Photoactive Drugs: An ab initio Study of a Cyclopropenone-Enediyne Drug Precursor

We present a contemporary mechanistic description of the light-driven conversion of cyclopropenone containing enediyne (CPE) precusors to ring-opened species amenable to further Bergman cyclization and formation of stable biradical species that have been proposed for use in light-induced cancer treatment. The transformation is rationalized in terms of (purely singlet state) Norrish type-I chemistry, wherein photoinduced opening of one C-C bond in the cyclopropenone ring facilitates non-adiabatic coupling to high levels of the ground state, subsequent loss of CO and Bergman cyclization of the enediyne intermediate to the cytotoxic target biradical species. Limited investigations of substituent effects on the ensuing photochemistry serve to vindicate the experimental choices of Popik and coworkers (J. Org. Chem., 2005, 70, 1297-1305). Specifically, replacing the phenyl moiety in the chosen model CPE by a 1,4-benzoquinone unit leads to a stronger, red-shifted parent absorption, and increases the exoergicity of the parent → biradical conversion.

Pharmacognosy and Its Role in the System of Profile Disciplines in Pharmacy

The primal discipline from which pharmacy has developed can be considered as pharmacognosy. This review defines pharmacognosy while reflecting on the latest development and discourse about its justifiability in the educational system in pharmaceutical faculties and the history of development of new drugs under the influence of pharmacognosy. The article defines the status quo of the pharmacognosy area, or more precisely its parts (biology, chemistry, production, and technology) and discusses their connections. It underlines the legitimacy of application of natural drugs in therapy, which is undeniable, and proves that whether a new drug was prepared either synthetically or isolated from a natural source is not important. The overview follows the basic requirements of pharmacognosy, especially its methodology (usage of faster and more effective phyto-analytical methods, reverse pharmacology, and reverse pharmacognosy, in silico methods). Pharmacognosy is confronted by three major challenges in the 21st century that can push it significantly forward: ethnopharmacological sources evaluation, evaluation of nutraceuticals, and pharmacognosy of marine organisms. The educational system of universities should correspond to these new demands. However, in some areas the educational system is not prepared to face the challenges of the time. The basic requirement is to adopt a complex attitude to biogenic material and utilize the connections of this complexity in the teaching of modern pharmacy.

Graph-based generative models for de Novo drug design

The discovery of new chemical entities is a crucial part of drug discovery, which requires the lead compounds to have desired properties to be pharmaceutically active. De novo drug design aims to generate and optimize novel ligands for macromolecular targets from scratch. The development of graph-based deep generative neural networks has provided a new method. In this review, we gave a brief introduction to graph representation and graph-based generative models for de novo drug design, summarized them as four architectures, and concluded each's characteristics. We also discussed generative models for scaffold- and fragment-based design and graph-based generative models' future directions.

A comprehensive review of feature based methods for drug target interaction prediction

Drug target interaction is a prominent research area in the field of drug discovery. It refers to the recognition of interactions between chemical compounds and the protein targets in the human body. Wet lab experiments to identify these interactions are expensive as well as time consuming. The computational methods of interaction prediction help limit the search space for these experiments. These computational methods can be divided into ligand based approaches, docking approaches and chemogenomic approaches. In this review, we aim to describe the various feature based chemogenomic methods for drug target interaction prediction. It provides a comprehensive overview of the various techniques, datasets, tools and metrics. The feature based methods have been categorized, explained and compared. A novel framework for drug target interaction prediction has also been proposed that aims to improve the performance of existing methods. To the best of our knowledge, this is the first comprehensive review focusing only on feature based methods of drug target interaction.

Paradigm shift - Metabolic transformation of docosahexaenoic and eicosapentaenoic acids to bioactives exemplify the promise of fatty acid drug discovery

Fatty acid drug discovery (FADD) is defined as the identification of novel, specialized bioactive mediators that are derived from fatty acids and have precise pharmacological/therapeutic potential. A number of reports indicate that dietary intake of omega-3 fatty acids and limited intake of omega-6 promotes overall health benefits. In 1929, Burr and Burr indicated the significant role of essential fatty acids for survival and functional health of many organs. In reference to specific dietary benefits of differential omega-3 fatty acids, docosahexaenoic and eicosapentaenoic acids (DHA and EPA) are transformed to monohydroxy, dihydroxy, trihydroxy, and other complex mediators during infection, injury, and exercise to resolve inflammation. The presented FADD approach describes the metabolic transformation of DHA and EPA in response to injury, infection, and exercise to govern uncontrolled inflammation. Metabolic transformation of DHA and EPA into a number of pro-resolving molecules exemplifies a novel, inexpensive approach compared to traditional, expensive drug discovery. DHA and EPA have been recommended for prevention of cardiovascular disease since 1970. Therefore, the FADD approach is relevant to cardiovascular disease and resolution of inflammation in many injury models. Future research demands identification of novel action targets, receptors for biomolecules, mechanism(s), and drug-interactions with resolvins in order to maintain homeostasis.

Method for Ultrarapid High-Content Screening for Biologically Active Chemicals Using Plant Pollen

Chemical genomics attracts considerable attention by offering crucial tools for plant biology to regulate plant growth and development. However, most chemical screens are time consuming and laborious, and require a high input of resources. Here we describe a broadly applicable method for the ultrarapid high-content phenotypic screening of small chemical compound libraries for new plant growth regulators. The assay is based on determination of pollen tube growth and can be completed in less than 8 h. Using this method, we identified novel inhibitors and modulators of plant growth and showed that compounds selected using a Nicotiana tabacum-based assay were biologically active in other plants, such as Arabidopsis thaliana.

Discovery of Boolean metabolic networks: integer linear programming based approach

Background

Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects.

Results

In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature.

Conclusions

Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at “http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip”.

Drug discovery and development for rare genetic disorders

Approximately 7,000 rare diseases affect millions of individuals in the United States. Although rare diseases taken together have an enormous impact, there is a significant gap between basic research and clinical interventions. Opportunities now exist to accelerate drug development for the treatment of rare diseases. Disease foundations and research centers worldwide focus on better understanding rare disorders. Here, the state-of-the-art drug discovery strategies for small molecules and biological approaches for orphan diseases are reviewed. Rare diseases are usually genetic diseases; hence, employing pharmacogenetics to develop treatments and using whole genome sequencing to identify the etiologies for such diseases are appropriate strategies to exploit. Beginning with high throughput screening of small molecules, the benefits and challenges of target-based and phenotypic screens are discussed. Explanations and examples of drug repurposing are given; drug repurposing as an approach to quickly move programs to clinical trials is evaluated. Consideration is given to the category of biologics which include gene therapy, recombinant proteins, and autologous transplants. Disease models, including animal models and induced pluripotent stem cells (iPSCs) derived from patients, are surveyed. Finally, the role of biomarkers in drug discovery and development, as well as clinical trials, is elucidated.

Anti-tubercular drug discovery: in silico implications and challenges

Tuberculosis (TB) has been reported as a major public health concern, especially in the developing countries. WHO report on tuberculosis 2016 shows a high mortality rate caused by TB leading to 1.8 million deaths worldwide (including deaths due to TB in HIV positive individuals), which is one of the top 10 causes of mortality in 2015. However, the main therapy used for the treatment of TB is still the Direct Observed Therapy Short-course (DOTS) that consists of four main first-line drugs. Due to the prolonged and unorganized use of these drugs, Mycobacterium tuberculosis (Mtb) has developed drug-resistance against them. To overcome this drug-resistance, efforts are continuously being made to develop new therapeutics. New drug-targets of Mtb are pursued by the researchers to develop their inhibitors. For this, new methodologies that comprise of the computational drug designing techniques are vigorously applied. A major limitation that is found with these techniques is the inability of the newly identified target-based inhibitors to inhibit the whole cell bacteria. A foremost factor for this limitation is the inability of these inhibitors to penetrate the bacterial cell wall. In this regard, various strategies to overcome this limitation have been discussed in detail in this review, along with new targets and new methodologies. A bunch of in silico tools available for the prediction of physicochemical properties that need to be explored to deal with the permeability issue of the Mtb inhibitors has also been discussed.

Alternative Animal and Non-Animal Models for Drug Discovery and Development: Bonus or Burden?

Mammalian models have served as a basis for R&D over the past decades. Nevertheless, these models are expensive, laborious, may yield results that cannot always be translated into the human in vivo situation and, more recently, have reverberated great social and ethical dilemmas. Hence, the prospect of changes in the global scientific scenario and the Three Rs principle (Reduction, Replacement and Refinement) have encouraged the development of alternative methods to the use of mammals. Despite the efforts, suitable alternative tests are not available in all areas of biomedical research, as regulatory acceptance requires time, prior validation and robust financial and scientific investment. In this perspective, we aim to shed light on the concepts, challenges and perspectives for implementation of innovative alternative animal and non-animal methods in scientific research. The applicability and meaningfulness of invertebrate animal models, in silico analysis and reverse pharmacology are discussed, among other aspects of relevance in today's scenario. Overall, the use of alternative models, including Artemia salina (brine shrimp), Caenorhabditis elegans (roundworm), Danio rerio (zebra fish), Drosophila melanogaster (fruit fly), Galleria mellonella (greater waxmoth) and in silico modelling, increased 909% from 1990 to 2015, as compared to 154% of conventional mammals in the same period. Thus, technological and scientific advancements in the fields of toxicology and drug development seem to have diminished the need for mammalian models. Today, however, mammals still remain critically indispensable to provide - in most cases -reliable data subsidizing and validating translation into the clinical setting.

Antibiotic drug discovery

Due to the threat posed by the increase of highly resistant pathogenic bacteria, there is an urgent need for new antibiotics; all the more so since in the last 20 years, the approval for new antibacterial agents had decreased. The field of natural product discovery has undergone a tremendous development over the past few years. This has been the consequence of several new and revolutionizing drug discovery and development techniques, which is initiating a ‘New Age of Antibiotic Discovery’. In this review, we concentrate on the most significant discovery approaches during the last and present years and comment on the challenges facing the community in the coming years.

Discovery and resupply of pharmacologically active plant-derived natural products: A review

Medicinal plants have historically proven their value as a source of molecules with therapeutic potential, and nowadays still represent an important pool for the identification of novel drug leads. In the past decades, pharmaceutical industry focused mainly on libraries of synthetic compounds as drug discovery source. They are comparably easy to produce and resupply, and demonstrate good compatibility with established high throughput screening (HTS) platforms. However, at the same time there has been a declining trend in the number of new drugs reaching the market, raising renewed scientific interest in drug discovery from natural sources, despite of its known challenges. In this survey, a brief outline of historical development is provided together with a comprehensive overview of used approaches and recent developments relevant to plant-derived natural product drug discovery. Associated challenges and major strengths of natural product-based drug discovery are critically discussed. A snapshot of the advanced plant-derived natural products that are currently in actively recruiting clinical trials is also presented. Importantly, the transition of a natural compound from a "screening hit" through a "drug lead" to a "marketed drug" is associated with increasingly challenging demands for compound amount, which often cannot be met by re-isolation from the respective plant sources. In this regard, existing alternatives for resupply are also discussed, including different biotechnology approaches and total organic synthesis. While the intrinsic complexity of natural product-based drug discovery necessitates highly integrated interdisciplinary approaches, the reviewed scientific developments, recent technological advances, and research trends clearly indicate that natural products will be among the most important sources of new drugs also in the future.

Foundational principles of classical Ayurveda research

Double-blind randomized controlled trials (RCTs) are viewed as the golden standard of drug research in Western medicine. However, RCTs are far from “golden” in many respects. They are impractical for many therapies, such as for surgeries and complex lifestyle changes. They encourage a one-size-fits-all approach to medical treatment that fails to address the huge diversity among individual patients in terms of their physical and emotional symptoms, social and cultural upbringing, and other factors. Perhaps, more importantly, they do not help doctors make the best medical decisions required to produce optimal patient outcomes. To guide a search for an alternate model of medical research, three principles based on Ayurveda, an ancient and powerful system of health care that has stood the test of time, are presented. These principles, arrived at after mining Ayurvedic epistemology, are: Inductive learning, whole systems thinking, and individually optimized therapy. In honor of the ancient sages or “Rishis,” whose voice is used to deliver Ayurvedic knowledge in the ancient texts of Ayurveda, these are referred to as the “Rishi principles.” Common research methodologies are examined for embodiment of these principles.

Small molecule screening in human induced pluripotent stem cell-derived terminal cell types

A need for better clinical outcomes has heightened interest in the use of physiologically relevant human cells in the drug discovery process. Patient-specific human induced pluripotent stem cells may offer a relevant, robust, scalable, and cost-effective model of human disease physiology. Small molecule high throughput screening in human induced pluripotent stem cell-derived cells with the intent of identifying novel therapeutic compounds is starting to influence the drug discovery process; however, the use of these cells presents many high throughput screening development challenges. This technology has the potential to transform the way drug discovery is performed.

Clinical relevance of target identity and biology: implications for drug discovery and development

Many of the most commonly used drugs precede techniques for target identification and drug specificity and were developed on the basis of efficacy and safety, an approach referred to as classical pharmacology and, more recently, phenotypic drug discovery. Although substantial gains have been made during the period of focus on target-based approaches, particularly in oncology, these approaches have suffered a high overall failure rate and lower productivity in terms of new drugs when compared with phenotypic approaches. This review considers the importance of target identity and biology in clinical practice from the prescriber's viewpoint. In evaluating influences on prescribing behavior, studies suggest that target identity and mechanism of action are not significant factors in drug choice. Rather, patients and providers consistently value efficacy, safety, and tolerability. Similarly, the Food and Drug Administration requires evidence of safety and efficacy for new drugs but does not require knowledge of drug target identity or target biology. Prescribers do favor drugs with novel mechanisms, but this preference is limited to diseases for which treatments are either not available or suboptimal. Thus, while understanding of drug target and target biology is important from a scientific perspective, it is not particularly important to prescribers, who prioritize efficacy and safety.

In silico and in vivo anti-malarial studies of 18β glycyrrhetinic acid from Glycyrrhiza glabra

Malaria is one of the most prevailing fatal diseases causing between 1.2 and 2.7 million deaths all over the world each year. Further, development of resistance against the frontline anti-malarial drugs has created an alarming situation, which requires intensive drug discovery to develop new, more effective, affordable and accessible anti-malarial agents possessing novel modes of action. Over the past few years triterpenoids from higher plants have shown a wide range of anti-malarial activities. As a part of our drug discovery program for anti-malarial agents from Indian medicinal plants, roots of Glycyrrhizaglabra were chemically investigated, which resulted in the isolation and characterization of 18β-glycyrrhetinic acid (GA) as a major constituent. The in vitro studies against P. falciparum showed significant (IC₅₀ 1.69µg/ml) anti-malarial potential for GA. Similarly, the molecular docking studies showed adequate docking (LibDock) score of 71.18 for GA and 131.15 for standard anti-malarial drug chloroquine. Further, in silico pharmacokinetic and drug-likeness studies showed that GA possesses drug-like properties. Finally, in vivo evaluation showed a dose dependent anti-malarial activity ranging from 68–100% at doses of 62.5–250mg/kg on day 8. To the best of our knowledge this is the first ever report on the anti-malarial potential of GA. Further work on optimization of the anti-malarial lead is under progress.

Phenotypic screens as a renewed approach for drug discovery

The significant reduction in the number of newly approved drugs in the past decade has been partially attributed to failures in discovery and validation of new targets. Evaluation of recently approved new drugs has revealed that the number of approved drugs discovered through phenotypic screens, an original drug screening paradigm, has exceeded those discovered through the molecular target-based approach. Phenotypic screening is thus gaining new momentum in drug discovery with the hope that this approach may revitalize drug discovery and improve the success rate of drug approval through the discovery of viable lead compounds and identification of novel drug targets.

How can natural products serve as a viable source of lead compounds for the development of new/novel anti-malarials?

Malaria continues to be an enormous global health challenge, with millions of new infections and deaths reported annually. This is partly due to the development of resistance by the malaria parasite to the majority of established anti-malarial drugs, a situation that continues to hamper attempts at controlling the disease. This has spurred intensive drug discovery endeavours geared towards identifying novel, highly active anti-malarial drugs, and the identification of quality leads from natural sources would greatly augment these efforts. The current reality is that other than compounds that have their foundation in historic natural products, there are no other compounds in drug discovery as part of lead optimization projects and preclinical development or further that have originated from a natural product start-point in recent years. This paper briefly presents both classical as well as some more modern, but underutilized, approaches that have been applied outside the field of malaria, and which could be considered in enhancing the potential of natural products to provide or inspire the development of anti-malarial lead compounds.