Drug Discovery Paradigm Research Articles

The Team that Just Wouldn't Quit and a New Paradigm in Cancer Drug Discovery.

Receiving the Lasker award for Clinical Medical Research is a staggering event. Perhaps equally staggering is the fact that as many as 2 million women worldwide have now been treated with Herceptin (trastuzumab), many of whom benefited from the treatment. This accomplishment is wonderfully rewarding for everyone who has been involved.

Antiepileptic drugs: evolution of our knowledge and changes in drug trials.

Clinical trials provide the evidence needed for rational use of medicines. The evolution of drug trials follows largely the evolution of regulatory requirements. This article summarizes methodological changes in antiepileptic drug trials and associated advances in knowledge starting from 1938, the year phenytoin was introduced and also the year when evidence of safety was made a requirement for the marketing of medicines in the United States. The first period (1938-1969) saw the introduction of over 20 new drugs for epilepsy, many of which did not withstand the test of time. Only few well controlled trials were completed in that period and trial designs were generally suboptimal due to methodological constraints. The intermediate period (1970-1988) did not see the introduction of any major new medication, but important therapeutic advances took place due to improved understanding of the properties of available drugs. The value of therapeutic drug monitoring and monotherapy were recognized during the intermediate period, which also saw major improvements in trial methodology. The last period (1989-2019) was dominated by the introduction of second-generation drugs, and further evolution in the design of monotherapy and adjunctive-therapy trials. The expansion of the pharmacological armamentarium has improved opportunities for tailoring drug treatment to the characteristics of the individual. However, there is still inadequate evidence from controlled trials to guide treatment selection for most epilepsy syndromes, particularly in children. Second-generation drugs had a very modest impact on drug resistance, and a change in paradigm for drug discovery and development is needed, focusing on treatments that target the causes and mechanisms of epilepsy rather than its symptoms. Testing potential disease modifying agents will require innovative trial designs and novel endpoints, and will hopefully lead to introduction of safer and more effective therapies.

Rational discovery of dual-indication multi-target PDE/Kinase inhibitor for precision anti-cancer therapy using structural systems pharmacology.

Many complex diseases such as cancer are associated with multiple pathological manifestations. Moreover, the therapeutics for their treatments often lead to serious side effects. Thus, it is needed to develop multi-indication therapeutics that can simultaneously target multiple clinical indications of interest and mitigate the side effects. However, conventional one-drug-one-gene drug discovery paradigm and emerging polypharmacology approach rarely tackle the challenge of multi-indication drug design. For the first time, we propose a one-drug-multi-target-multi-indication strategy. We develop a novel structural systems pharmacology platform 3D-REMAP that uses ligand binding site comparison and protein-ligand docking to augment sparse chemical genomics data for the machine learning model of genome-scale chemical-protein interaction prediction. Experimentally validated predictions systematically show that 3D-REMAP outperforms state-of-the-art ligand-based, receptor-based, and machine learning methods alone. As a proof-of-concept, we utilize the concept of drug repurposing that is enabled by 3D-REMAP to design dual-indication anti-cancer therapy. The repurposed drug can demonstrate anti-cancer activity for cancers that do not have effective treatment as well as reduce the risk of heart failure that is associated with all types of existing anti-cancer therapies. We predict that levosimendan, a PDE inhibitor for heart failure, inhibits serine/threonine-protein kinase RIOK1 and other kinases. Subsequent experiments and systems biology analyses confirm this prediction, and suggest that levosimendan is active against multiple cancers, notably lymphoma, through the direct inhibition of RIOK1 and RNA processing pathway. We further develop machine learning models to predict cancer cell-line’s and a patient’s response to levosimendan. Our findings suggest that levosimendan can be a promising novel lead compound for the development of safe, effective, and precision multi-indication anti-cancer therapy. This study demonstrates the potential of structural systems pharmacology in designing polypharmacology for precision medicine. It may facilitate transforming the conventional one-drug-one-gene-one-disease drug discovery process and single-indication polypharmacology approach into a new one-drug-multi-target-multi-indication paradigm for complex diseases.

Zebrafish as an Emerging Model for Bioassay-Guided Natural Product Drug Discovery for Neurological Disorders.

Most neurodegenerative diseases are currently incurable, with large social and economic impacts. Recently, there has been renewed interest in investigating natural products in the modern drug discovery paradigm as novel, bioactive small molecules. Moreover, the discovery of potential therapies for neurological disorders is challenging and involves developing optimized animal models for drug screening. In contemporary biomedicine, the growing need to develop experimental models to obtain a detailed understanding of malady conditions and to portray pioneering treatments has resulted in the application of zebrafish to close the gap between in vitro and in vivo assays. Zebrafish in pharmacogenetics and neuropharmacology are rapidly becoming a widely used organism. Brain function, dysfunction, genetic, and pharmacological modulation considerations are enhanced by both larval and adult zebrafish. Bioassay-guided identification of natural products using zebrafish presents as an attractive strategy for generating new lead compounds. Here, we see evidence that the zebrafish’s central nervous system is suitable for modeling human neurological disease and we review and evaluate natural product research using zebrafish as a vertebrate model platform to systematically identify bioactive natural products. Finally, we review recently developed zebrafish models of neurological disorders that have the potential to be applied in this field of research.

Design, Synthesis, and Biological Evaluation of 4-Methyl Quinazoline Derivatives as Anticancer Agents Simultaneously Targeting Phosphoinositide 3-Kinases and Histone Deacetylases.

Polypharmacology is a promising paradigm in modern drug discovery. Herein, we have discovered a series of novel PI3K and HDAC dual inhibitors in which the hydroxamic acid moiety as the zinc binding functional group was introduced to a quinazoline-based PI3K pharmacophore through an appropriate linker. Systematic structure-activity relationship studies resulted in lead compounds 23 and 36 that simultaneously inhibited PI3K and HDAC with nanomolar potencies and demonstrated favorable antiproliferative activities. Compounds 23 and 36 efficiently modulated the expression of p-AKT and Ac-H3, arrested the cell cycle, and induced apoptosis in HCT116 cancer cells. Following pharmacokinetic studies, 23 was further evaluated in HCT116 and HGC-27 xenograft models to show significant in vivo anticancer efficacies with tumor growth inhibitions of 45.8% (po, 150 mg/kg) and 62.6% (ip, 30 mg/kg), respectively. Overall, this work shows promise in discovering new anticancer therapeutics by the approach of simultaneously targeting PI3K and HDAC pathways with a single molecule.

Efficacy of Cannabinoids in a Pre-Clinical Drug-Screening Platform for Alzheimer's Disease.

Finding a therapy for Alzheimer's disease (AD) is perhaps the greatest challenge for modern medicine. The chemical scaffolds of many drugs in the clinic today are based upon natural products from plants, yet Cannabis has not been extensively examined as a source of potential AD drug candidates. Here, we determine if a number of non-psychoactive cannabinoids are neuroprotective in a novel pre-clinical AD and neurodegeneration drug-screening platform that is based upon toxicities associated with the aging brain. This drug discovery paradigm has yielded several compounds in or approaching clinical trials for AD. Eleven cannabinoids were assayed for neuroprotection in assays that recapitulate proteotoxicity, loss of trophic support, oxidative stress, energy loss, and inflammation. These compounds were also assayed for their ability to remove intraneuronal amyloid and subjected to a structure-activity relationship analysis. Pairwise combinations were assayed for their ability to synergize to produce neuroprotective effects that were greater than additive. Nine of the 11 cannabinoids have the ability to protect cells in four distinct phenotypic neurodegeneration screening assays, including those using neurons that lack CB1 and CB2 receptors. They are able to remove intraneuronal Aβ, reduce oxidative damage, and protect from the loss of energy or trophic support. Structure-activity relationship (SAR) data show that functional antioxidant groups such as aromatic hydroxyls are necessary but not sufficient for neuroprotection. Therefore, there is a need to focus upon CB1 agonists that have these functionalities if neuroprotection is the goal. Pairwise combinations of THC and CBN lead to a synergistic neuroprotective interaction. Together, these results significantly extend the published data by showing that non-psychoactive cannabinoids are potential lead drug candidates for AD and other neurodegenerative diseases.

Epilepsy Surgery in 2019 : A Time to Change.

Epilepsy has been known to humankind since antiquity. The surgical treatment of epilepsy began in the early days of neurosurgery and has developed greatly. Many surgical procedures have stood the test of time. However, clinicians treating epilepsy patients are now witnessing a huge tide of change. In 2017, the classification system for seizure and epilepsy types was revised nearly 36 years after the previous scheme was released. The actual difference between these systems may not be large, but there have been many conceptual changes, and clinicians must bid farewell to old terminology. Paradigms in drug discovery are changing, and novel antiseizure drugs have been introduced for clinical use. In particular, drugs that target genetic changes harbor greater therapeutic potential than previous screening-based compounds. The concept of focal epilepsy has been challenged, and now epilepsy is regarded as a network disorder. With this novel concept, stereotactic electroencephalography (SEEG) is becoming increasingly popular for the evaluation of dysfunctioning neuronal networks. Minimally invasive ablative therapies using SEEG electrodes and neuromodulatory therapies such as deep brain stimulation and vagus nerve stimulation are widely applied to remedy dysfunctional epilepsy networks. The use of responsive neurostimulation is currently off-label in children with intractable epilepsy.

Emerging roles of allosteric modulators in the regulation of protein-protein interactions (PPIs): A new paradigm for PPI drug discovery.

Protein-protein interactions (PPIs) are closely implicated in various types of cellular activities and are thus pivotal to health and disease states. Given their fundamental roles in a wide range of biological processes, the modulation of PPIs has enormous potential in drug discovery. However, owing to the general properties of large, flat, and featureless interfaces of PPIs, previous attempts have demonstrated that the generation of therapeutic agents targeting PPI interfaces is challenging, rendering them almost "undruggable" for decades. To date, rapid progress in chemical and structural biology techniques has promoted the exploitation of allostery as a novel approach in drug discovery. By attaching to allosteric sites that are topologically and spatially distinct from PPI interfaces, allosteric modulators can achieve improved physiochemical properties. Thus, allosteric modulators may represent an alternative strategy to target intractable PPIs and have attracted intense pharmaceutical interest. In this review, we first briefly introduce the characteristics of PPIs and then present different approaches for investigating PPIs, as well as the latest methods for modulating PPIs. Importantly, we comprehensively review the recent progress in the development of allosteric modulators to inhibit or stabilize PPIs. Finally, we conclude with future perspectives on the discovery of allosteric PPI modulators, especially the application of computational methods to aid in allosteric PPI drug discovery.

Translational Framework Predicting Tumour Response in Gemcitabine-Treated Patients with Advanced Pancreatic and Ovarian Cancer from Xenograft Studies.

The aim of this evaluation was to predict tumour response to gemcitabine in patients with advanced pancreas or ovarian cancer using pre-clinical data obtained from xenograft tumour-bearing mice. The approach consisted of building a translational model combining pre-clinical pharmacokinetic-pharmacodynamic (PKPD) models and parameters, with dosing paradigms used in the clinics along with clinical PK models to derive tumour profiles in humans driving overall survival. Tumour growth inhibition simulations were performed using drug effect parameters obtained from mice, system parameters obtained from mice after appropriate scaling, patient PK models for gemcitabine and carboplatin, and the standard dosing schedules given in the clinical scenario for both types of cancers. Tumour profiles in mice were scaled by body weight to their equivalent values in humans. As models for survival in humans showed that tumour size was the main driver of the hazard rate, it was possible to describe overall survival in pancreatic and ovarian cancer patients. Simulated tumour dynamics in pancreatic and ovarian cancer patients were evaluated using available data from clinical trials. Furthermore, calculated metrics showed values (maximal tumour regression [0-17%] and tumour size ratio at week 12 with respect to baseline [- 9, - 4.5]) in the range of those predicted with the clinical PKPD models. The model-informed Drug Discovery and Development paradigm has been successfully applied retrospectively to gemcitabine data, through a semi-mechanistic translational approach, describing the time course of the tumour response in patients from pre-clinical studies.

Multidisciplinary approaches for targeting the secretase protein family as a therapeutic route for Alzheimer's disease.

The continual increase of the aging population worldwide renders Alzheimer's disease (AD) a global prime concern. Several attempts have been focused on understanding the intricate complexity of the disease's development along with the on- andgoing search for novel therapeutic strategies. Incapability of existing AD drugs to effectively modulate the pathogenesis or to delay the progression of the disease leads to a shift in the paradigm of AD drug discovery. Efforts aimed at identifying AD drugs have mostly focused on the development of disease-modifying agents in which effects are believed to be long lasting. Of particular note, the secretase enzymes, a group of proteases responsible for the metabolism of the β-amyloid precursor protein (βAPP) and β-amyloid (Aβ) peptides production, have been underlined for their promising therapeutic potential. This review article attempts to comprehensively cover aspects related to the identification and use of drugs targeting the secretase enzymes. Particularly, the roles of secretases in the pathogenesis of AD and their therapeutic modulation are provided herein. Moreover, an overview of the drug development process and the contribution of computational (in silico) approaches for facilitating successful drug discovery are also highlighted along with examples of relevant computational works. Promising chemical scaffolds, inhibitors, and modulators against each class of secretases are also summarized herein. Additionally, multitarget secretase modulators are also taken into consideration in light of the current growing interest in the polypharmacology of complex diseases. Finally, challenging issues and future outlook relevant to the discovery of drugs targeting secretases are also discussed.

Heuristics and Explanation in Translational Medicine.

The reigning paradigm of rational drug discovery in translational medicine attempts to exploit biological theories and pathophysiological explanations to identify novel drug targets and therapeutic strategies. Yet because many theories in medicine are either incomplete (at best) or false (at worst), relying on theoretical explanations can have some puzzling and troubling consequences. New drugs may be shown to be effective in clinical trials and receive regulatory approval despite a faulty explanation for why they are effective. If physicians rely on this explanation to make treatment decisions, it can lead to systematic misdiagnoses and patient harm. For research programs, underdetermination may shield faulty biomedical explanations, and this can result in wasted research resources and avoidable burdens on human subjects. These problematic features of biomedical explanations can be resolved by reconceiving the epistemology of translational medicine in terms of heuristics.

Brief history of anti-seizure drug development.

SummaryThe mainstay of therapy for epilepsy is anti‐seizure drugs (ASDs, also referred to as anticonvulsants and anti‐epileptic medications). Through much of the past century, only a handful for ASDs were available for clinical use. However, with the creation of the U.S. National Institutes of Health/National Institute of Neurological Disorders and Stroke (NINDS)–sponsored Anticonvulsant Screening Program (ASP), coupled with the emergence of high‐throughput screening platforms and methodologies, and advances in our understanding of the fundamental neurobiology of epilepsy, ASD development has greatly accelerated over the past 25 years. More than 18 new ASDs have been approved for clinical use since the inception of the ASP. Despite this remarkable success and the emergence of drugs possessing more favorable pharmacokinetic profiles that act on novel molecular targets, there has been increasing recognition that the paradigms for drug discovery have not yielded significant improvements in therapeutic efficacy, and that disease modification (i.e., anti‐epileptogenesis), among other challenges, must be addressed. Thus, with the renewed framework and mission of improving the lives of people with epilepsy, the name of the ASP was changed to the Epilepsy Therapy Screening Program (ETSP). This review briefly summarizes the history of ASD development and outlines some of the challenges and opportunities for the next generation of drug therapies for the epilepsy field.

Cardiovascular disease models: A game changing paradigm in drug discovery and screening

Cardiovascular disease is the leading cause of death worldwide. Although investment in drug discovery and development has been sky-rocketing, the number of approved drugs has been declining. Cardiovascular toxicity due to therapeutic drug use claims the highest incidence and severity of adverse drug reactions in late-stage clinical development. Therefore, to address this issue, new, additional, replacement and combinatorial approaches are needed to fill the gap in effective drug discovery and screening. The motivation for developing accurate, predictive models is twofold: first, to study and discover new treatments for cardiac pathologies which are leading in worldwide morbidity and mortality rates; and second, to screen for adverse drug reactions on the heart, a primary risk in drug development. In addition to in vivo animal models, in vitro and in silico models have been recently proposed to mimic the physiological conditions of heart and vasculature. Here, we describe current in vitro, in vivo, and in silico platforms for modelling healthy and pathological cardiac tissues and their advantages and disadvantages for drug screening and discovery applications. We review the pathophysiology and the underlying pathways of different cardiac diseases, as well as the new tools being developed to facilitate their study. We finally suggest a roadmap for employing these non-animal platforms in assessing drug cardiotoxicity and safety.

Network-based drug repositioning: A novel strategy for discovering potential antidepressants and their mode of action

Current target-oriented paradigm for novel antidepressant discovery has been difficult to succeed and the failures always bring huge economic losses. Although abundant ledge of disease related genes and drug action targets has been accumulated, the successful application of the knowledge for new drug discovery is limited. Here, we predicted and validated potential antidepressants and molecular targets from DrugBank recorded drugs using a novel network-based drug repositioning approach. This approach predicted relationships between drug and targets through network-based integration of drug chemical similarity, therapeutic similarity and protein–protein interactions. We predicted genome-wide relations of drugs and targets, and then screened drugs that connect to depression-related targets of known antidepressants. Six drugs were predicted and experimentally validated to have antidepressant-like effects in the tail suspension test (TST) and forced swimming test (FST) in mice. Alverine, which is a gastrointestinal antispasmodic drug, was further validated to display antidepressant-like effects in the learned helplessness and chronic unpredictable stress models of depression. Four targets, including serotonin transporter, norepinephrine transporter, serotonin 1A receptor and serotonin 2A receptor, were included in the predictable system and confirmed as primary sites of action for alverine. The results suggest that alverine may be an effective antidepressant drug and the network-based drug repositioning may be a promising drug discovery paradigm for complex multi-genetic diseases such as depression.

Polypharmacology of Berberine Based on Multi-Target Binding Motifs.

Background: Polypharmacology is emerging as the next paradigm in drug discovery. However, considerable challenges still exist for polypharmacology modeling. In this study, we developed a rational design to identify highly potential targets (HPTs) for polypharmacological drugs, such as berberine.Methods and Results: All the proven co-crystal structures locate berberine in the active cavities of a redundancy of aromatic, aliphatic, and acidic residues. The side chains from residues provide hydrophobic and electronic interactions to aid in neutralization for the positive charge of berberine. Accordingly, we generated multi-target binding motifs (MBM) for berberine, and established a new mathematical model to identify HPTs based on MBM. Remarkably, the berberine MBM was embodied in 13 HPTs, including beta-secretase 1 (BACE1) and amyloid-β1-42 (Aβ1-42). Further study indicated that berberine acted as a high-affinity BACE1 inhibitor and prevented Aβ1-42 aggregation to delay the pathological process of Alzheimer’s disease.Conclusion: Here, we proposed a MBM-based drug-target space model to analyze the underlying mechanism of multi-target drugs against polypharmacological profiles, and demonstrated the role of berberine in Alzheimer’s disease. This approach can be useful in derivation of rules, which will illuminate our understanding of drug action in diseases.

The Meisenheimer Complex as a Paradigm in Drug Discovery: Reversible Covalent Inhibition through C67 of the ATP Binding Site of PLK1

The polo kinase family are important oncology targets that act in regulating entry into and progression through mitosis. Structure-guided discovery of a new class of inhibitors of Polo-like kinase 1 (PLK1) catalytic activity that interact with Cys67 of the ATP binding site is described. Compounds containing the benzothiazole N-oxide scaffold not only bind covalently to this residue, but are reversible inhibitors through the formation of Meisenheimer complexes. This mechanism of kinase inhibition results in compounds that can target PLK1 with high selectivity, while avoiding issues with irreversible covalent binding and interaction with other thiol-containing molecules in the cell. Due to renewed interest in covalent drugs and the plethora of potential drug targets, these represent prototypes for the design of kinase inhibitory compounds that achieve high specificity through covalent interaction and yet still bind reversibly to the ATP cleft, a strategy that could be applied to avoid issues with conventional covalent binders.

A New Approach to Treating Neurodegenerative Otologic Disorders.

Hearing loss, the most common neurological disorder and the fourth leading cause of years lived with disability, can have profound effects on quality of life. The impact of this “invisible disability,” with significant consequences, economic and personal, is most substantial in low- and middle-income countries, where >80% of affected people live. Given the importance of hearing for communication, enjoyment, and safety, with up to 500 million affected globally at a cost of nearly $800 billion/year, research on new approaches toward prevention and treatment is attracting increased attention. The consequences of noise pollution are largely preventable, but irreversible hearing loss can result from aging, disease, or drug side effects. Once damage occurs, treatment relies on hearing aids and cochlear implants. Preventing, delaying, or reducing some degree of hearing loss may be possible by avoiding excessive noise and addressing major contributory factors such as cardiovascular risk. However, given the magnitude of the problem, these interventions alone are unlikely to be sufficient. Recent advances in understanding principal mechanisms that govern hearing function, together with new drug discovery paradigms designed to identify efficacious therapies, bode well for pharmaceutical intervention. This review surveys various causes of loss of auditory function and discusses potential neurological underpinnings, including mitochondrial dysfunction. Mitochondria mitigate cell protection, survival, and function and may succumb to cumulative degradation of energy production and performance; the end result is cell death. Energy-demanding neurons and vestibulocochlear hair cells are vulnerable to mitochondrial dysfunction, and hearing impairment and deafness are characteristic of neurodegenerative mitochondrial disease phenotypes. Beyond acting as cellular powerhouses, mitochondria regulate immune responses to infections, and studies of this phenomenon have aided in identifying nuclear factor kappa B and nuclear factor erythroid 2-related factor 2/antioxidant response element signaling as targets for discovery of otologic drugs, respectively, suppressing or upregulating these pathways. Treatment with free radical scavenging antioxidants is one therapeutic approach, with lipoic acid and corresponding carnitine esters exhibiting improved biodistribution and other features showing promise. These compounds are also histone deacetylase (HDAC) inhibitors, adding epigenetic modulation to the mechanistic milieu through which they act. These data suggest that new drugs targeting mitochondrial dysfunction and modulating epigenetic pathways via HDAC inhibition or other mechanisms hold great promise.

Cellular target engagement: a new paradigm in drug discovery.

Cellular target engagement: a new paradigm in drug discovery.

Correction to: Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.

The name of the corresponding author should be 'Xiang-Qun Xie', rather than 'Xiang-Qun Sean Xie'.

Acoplamiento Molecular: Avances Recientes y Retos

El acoplamiento molecular automatizado tiene como objetivo proponer un modelo de unión entre dos moléculas. Este método ha sido útil en química farmacéutica y en el descubrimiento de nuevos fármacos por medio del entendimiento de las fuerzas de interacción involucradas en el reconocimiento molecular. Durante los últimos 20 años se ha modificado extensamente la técnica de acoplamiento molecular dando resultados precisos en la predicción de los modos de unión. Sin embargo, hay algunas áreas que requieren ser mejoradas substancialmente. En este trabajo se revisan principios, técnicas y algoritmos usados en los programas computacionales del acoplamiento molecular con enfoque en la interacción proteína-ligando aplicado al descubrimiento de nuevos fármacos. También se discuten las estrategias dirigidas a solucionar los principales retos de esta técnica computacional.