Prodrug Strategies for Critical Drug Developability Issues: Part I

Drug development is a very time, capital, and labor-intensive process. It was anticipated that bringing a novel chemical entity to market would take over a billion dollars and around 14 years [1]. In addition, drug development is characterized by a very high attrition rate both in preclinical and clinical studies. It was reported that only 40% of drug candidates with the most drug-like properties could make their way into clinical trials, and only 10% of these can eventually reach FDA approval [2]. After analyzing the data from seven UK‐based pharmaceutical companies from 1964 through 1985, Prentis et al. found that 39% of failure was attributed to poor pharmacokinetic (PK) profiles in humans, 29% was attributed to a lack of clinical efficacy, 21% was attributed to toxicity and adverse effects, and about 6% was attributed to commercial limitations [3]. When a drug candidate is identified with one of these issues (except the commercial limitations), normally, a new round of structureactivity or structure-property relationship (SAR/SPR) studies is carried out to generate a new chemical entity with improved profiles, and in most cases, such a process is time and labor-intensive. Alternatively, prodrug strategy can be leveraged to efficiently address associated drug developability issues without making enormous derivatives. Prodrug strategy has been demonstrated to be very successful and fruitful in drug development, with around 20% of approved drugs from 2008 through 2020 being clarified as prodrugs [4]. In recent years, prodrug strategy has also been leveraged to address the delivery issues associated with gasotransmitters, including NO, H2S, CO as well as SO2 [5-8]. In this thematic issue, six excellent reviews were included, focusing on varied prodrug strategies in addressing different drug developability issues associated with anticancer drugs, central nervous system (CNS) drugs, and gasotransmitters....

Download Full-text

Cheminformatics techniques in antimalarial drug discovery and development from natural products 1: basic concepts

Physical Sciences Reviews ◽

10.1515/psr-2018-0130 ◽

2019 ◽

Vol 4 (7) ◽

Author(s):

Samuel Egieyeh ◽

Sarel F. Malan ◽

Alan Christoffels

Keyword(s):

Natural Products ◽

Drug Development ◽

Antimalarial Drug ◽

Drug Discovery And Development ◽

Drug Candidates ◽

Structure Activity ◽

Preclinical And Clinical Studies ◽

Research Cost

Abstract A large number of natural products, especially those used in ethnomedicine of malaria, have shown varying in vitro antiplasmodial activities. Facilitating antimalarial drug development from this wealth of natural products is an imperative and laudable mission to pursue. However, limited manpower, high research cost coupled with high failure rate during preclinical and clinical studies might militate against the pursuit of this mission. These limitations may be overcome with cheminformatic techniques. Cheminformatics involves the organization, integration, curation, standardization, simulation, mining and transformation of pharmacology data (compounds and bioactivity) into knowledge that can drive rational and viable drug development decisions. This chapter will review the application of cheminformatics techniques (including molecular diversity analysis, quantitative-structure activity/property relationships and Machine learning) to natural products with in vitro and in vivo antiplasmodial activities in order to facilitate their development into antimalarial drug candidates and design of new potential antimalarial compounds.

Download Full-text

Prediction of Drug Approval After Phase I Clinical Trials in Oncology: RESOLVED2

JCO Clinical Cancer Informatics ◽

10.1200/cci.19.00023 ◽

2019 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Guillaume Beinse ◽

Virgile Tellier ◽

Valentin Charvet ◽

Eric Deutsch ◽

Isabelle Borget ◽

...

Keyword(s):

Machine Learning ◽

Clinical Trials ◽

Drug Development ◽

Phase I ◽

Attrition Rate ◽

Phase I Clinical Trials ◽

Test Set ◽

Free Survival ◽

Fda Approval ◽

Independent Test

PURPOSE Drug development in oncology currently is facing a conjunction of an increasing number of antineoplastic agents (ANAs) candidate for phase I clinical trials (P1CTs) and an important attrition rate for final approval. We aimed to develop a machine learning algorithm (RESOLVED2) to predict drug development outcome, which could support early go/no-go decisions after P1CTs by better selection of drugs suitable for further development. METHODS PubMed abstracts of P1CTs reporting on ANAs were used together with pharmacologic data from the DrugBank5.0 database to model time to US Food and Drug Administration (FDA) approval (FDA approval-free survival) since the first P1CT publication. The RESOLVED2 model was trained with machine learning methods. Its performance was evaluated on an independent test set with weighted concordance index (IPCW). RESULTS We identified 462 ANAs from PubMed that matched with DrugBank5.0 (P1CT publication dates 1972 to 2017). Among 1,411 variables, 28 were used by RESOLVED2 to model the FDA approval-free survival, with an IPCW of 0.89 on the independent test set. RESOLVED2 outperformed a model that was based on efficacy/toxicity (IPCW, 0.69). In the test set at 6 years of follow-up, 73% (95% CI, 49% to 86%) of drugs predicted to be approved were approved, whereas 92% (95% CI, 87% to 98%) of drugs predicted to be nonapproved were still not approved (log-rank P < .001). A predicted approved drug was 16 times more likely to be approved than a predicted nonapproved drug (hazard ratio, 16.4; 95% CI, 8.40 to 32.2). CONCLUSION As soon as P1CT completion, RESOLVED2 can predict accurately the time to FDA approval. We provide the proof of concept that drug development outcome can be predicted by machine learning strategies.

Download Full-text

Biomedical Graph Visualizer for Identifying Drug Candidates

10.1101/2020.11.27.368811 ◽

2020 ◽

Author(s):

Ashton Teng ◽

Blanca Villanueva ◽

Derek Jow ◽

Shih-Cheng (Mars) Huang ◽

Samantha N. Piekos ◽

...

Keyword(s):

Drug Development ◽

De Novo ◽

Drug Candidate ◽

Knowledge Graph ◽

Major Barrier ◽

Drug Candidates ◽

Novel Treatments ◽

Computational Discovery ◽

Complex Relationships ◽

User Friendly

1.AbstractMillions of Americans suffer from illnesses with non-existent or ineffective drug treatment. Identifying plausible drug candidates is a major barrier to drug development due to the large amount of time and resources required; approval can take years when people are suffering now. While computational tools can expedite drug candidate discovery, these tools typically require programming expertise that many biologists lack. Though biomedical databases continue to grow, they have proven difficult to integrate and maintain, and non-programming interfaces for these data sources are scarce and limited in capability. This creates an opportunity for us to present a suite of user-friendly software tools to aid computational discovery of novel treatments through de novo discovery or repurposing. Our tools eliminate the need for researchers to acquire computational expertise by integrating multiple databases and offering an intuitive graphical interface for analyzing these publicly available data. We built a computational knowledge graph focused on biomedical concepts related to drug discovery, designed visualization tools that allow users to explore complex relationships among entities in the graph, and served these tools through a free and user-friendly web interface. We show that users can conduct complex analyses with relative ease and that our knowledge graph and algorithms recover approved repurposed drugs. Our evaluation indicates that our method provides an intuitive, easy, and effective toolkit for discovering drug candidates. We show that our toolkit makes computational analysis for drug development more accessible and efficient and ultimately plays a role in bringing effective treatments to all patients.Our application is hosted at: https://biomedical-graph-visualizer.wl.r.appspot.com/

Download Full-text

Use of Microdosing and Accelerator Mass Spectrometry To Evaluate the Pharmacokinetic Linearity of a Novel Tricyclic GyrB/ParE Inhibitor in Rats

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.03300-14 ◽

2014 ◽

Vol 58 (11) ◽

pp. 6477-6483 ◽

Cited By ~ 4

Author(s):

Michael A. Malfatti ◽

Victoria Lao ◽

Courtney L. Ramos ◽

Voon S. Ong ◽

Kenneth W. Turteltaub

Keyword(s):

Drug Development ◽

Development Process ◽

Dose Range ◽

Area Under The Curve ◽

Drug Candidate ◽

Drug Development Process ◽

Sprague Dawley ◽

Drug Candidates ◽

Biological Fate ◽

Therapeutic Doses

ABSTRACTDetermining the pharmacokinetics (PKs) of drug candidates is essential for understanding their biological fate. The ability to obtain human PK information early in the drug development process can help determine if future development is warranted. Microdosing was developed to assess human PKs, at ultra-low doses, early in the drug development process. Microdosing has also been used in animals to confirm PK linearity across subpharmacological and pharmacological dose ranges. The current study assessed the PKs of a novel antimicrobial preclinical drug candidate (GP-4) in rats as a step toward human microdosing studies. Dose proportionality was determined at 3 proposed therapeutic doses (3, 10, and 30 mg/kg of body weight), and PK linearity between a microdose and a pharmacological dose was assessed in Sprague-Dawley rats. Plasma PKs over the 3 pharmacological doses were proportional. Over the 10-fold dose range, the maximum concentration in plasma and area under the curve (AUC) increased 9.5- and 15.8-fold, respectively. PKs from rats dosed with a14C-labeled microdose versus a14C-labeled pharmacological dose displayed dose linearity. In the animals receiving a microdose and the therapeutically dosed animals, the AUCs from time zero to infinity were 2.6 ng · h/ml and 1,336 ng · h/ml, respectively, and the terminal half-lives were 5.6 h and 1.4 h, respectively. When the AUC values were normalized to a dose of 1.0 mg/kg, the AUC values were 277.5 ng · h/ml for the microdose and 418.2 ng · h/ml for the pharmacological dose. This 1.5-fold difference in AUC following a 300-fold difference in dose is considered linear across the dose range. On the basis of the results, the PKs from the microdosed animals were considered to be predictive of the PKs from the therapeutically dosed animals.

Download Full-text

In Vitro Immune Organs-on-Chip for Drug Development: A Review

Pharmaceutics ◽

10.3390/pharmaceutics10040278 ◽

2018 ◽

Vol 10 (4) ◽

pp. 278 ◽

Cited By ~ 16

Author(s):

Aya Shanti ◽

Jeremy Teo ◽

Cesare Stefanini

Keyword(s):

Immune System ◽

Drug Development ◽

Attrition Rate ◽

Human Immune System ◽

Drug Candidates ◽

Immune Organs ◽

On Chip ◽

High Attrition Rate

The current drug development practice lacks reliable and sensitive techniques to evaluate the immunotoxicity of drug candidates, i.e., their effect on the human immune system. This, in part, has resulted in a high attrition rate for novel drugs candidates. Organ-on-chip devices have emerged as key tools that permit the study of human physiology in controlled in vivo simulating environments. Furthermore, there has been a growing interest in developing the so called “body-on-chip” devices to better predict the systemic effects of drug candidates. This review describes existing biomimetic immune organs-on-chip, highlights their physiological relevance to drug development and discovery and emphasizes the need for developing comprehensive immune system-on-chip models. Such immune models can enhance the performance of novel drug candidates during clinical trials and contribute to reducing the high attrition rate as well as the high cost associated with drug development.

Download Full-text

Endogenous Biomarkers for SLC Transporter-Mediated Drug-Drug Interaction Evaluation

Molecules ◽

10.3390/molecules26185500 ◽

2021 ◽

Vol 26 (18) ◽

pp. 5500

Author(s):

Yang Li ◽

Zahra Talebi ◽

Xihui Chen ◽

Alex Sparreboom ◽

Shuiying Hu

Keyword(s):

Drug Development ◽

Drug Transport ◽

Development Process ◽

Membrane Transporters ◽

Drug Candidates ◽

Endogenous Substrates ◽

Slc Transporter ◽

Preclinical And Clinical Studies ◽

Uptake Transporters ◽

Early Phases

Membrane transporters play an important role in the absorption, distribution, metabolism, and excretion of xenobiotic substrates, as well as endogenous compounds. The evaluation of transporter-mediated drug-drug interactions (DDIs) is an important consideration during the drug development process and can guide the safe use of polypharmacy regimens in clinical practice. In recent years, several endogenous substrates of drug transporters have been identified as potential biomarkers for predicting changes in drug transport function and the potential for DDIs associated with drug candidates in early phases of drug development. These biomarker-driven investigations have been applied in both preclinical and clinical studies and proposed as a predictive strategy that can be supplanted in order to conduct prospective DDIs trials. Here we provide an overview of this rapidly emerging field, with particular emphasis on endogenous biomarkers recently proposed for clinically relevant uptake transporters.

Download Full-text

In silico synergistic drug repurposing for combating novel coronavirus (COVID-19) outbreaks

10.21203/rs.3.rs-21849/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Minjee Kim ◽

Young Bong Kim

Keyword(s):

Drug Development ◽

Drug Combination ◽

Drug Repurposing ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Ppi Network ◽

Target List ◽

Drug Candidates ◽

Novel Coronavirus ◽

Approved Drugs

Abstract As the number of novel coronavirus (COVID-19) cases continues to rise, there is a global need for rapid drug development. In this study, we propose a systems pharmacology approach to reposition FDA-approved drug candidates for coronavirus, identify targets and suggest a synergistic drug combination using network pharmacology. We collected 67 genes associated with coronavirus, performed an enrichment analysis to obtain coronavirus-associated disease- pathway and constructed protein-protein interaction (PPI) network based on 67 genes. Total 37 significant disease-pathways were retrieved, and associated FDA-approved drugs were listed for drug repurposing candidates. Our PPI network showed 51 targets from 67 genes and identified IL6 and TNF as potential targets for coronavirus. From the FDA drug list, we selected four drugs that are experimentally used or studied for coronavirus to construct two- drug combinations. From six drug-drug networks, we identified hydroxychloroquine + ribavirin combination had the highest number of overlapping targets (IL6, IL2, IL10, CASP3, IFNA1) from PPI network target list, suggesting a potent synergistic drug combination for coronavirus. With the aim to support the rapid drug development, we suggest a new approach using systems-level drug repurposing for COVID-19 treatment.

Download Full-text

Drug Repurposing in Oncotherapeutics

Drug Repurposing - Hypothesis, Molecular Aspects and Therapeutic Applications ◽

10.5772/intechopen.92302 ◽

2020 ◽

Author(s):

Alkeshkumar Patel

Keyword(s):

Drug Development ◽

Drug Repurposing ◽

Hematologic Malignancies ◽

Fda Approval ◽

Anti Cancer ◽

Future Burden ◽

Regulatory Bodies ◽

Life Improvement ◽

Literature Based Discovery ◽

Approved Drugs

Repurposing or repositioning means validating and application of previously approved drugs in the treatment of another disease that might be relevant or irrelevant to existing use in disease based on the principle of polypharmacology. Repurposed drugs are already well documented for pharmacokinetic, pharmacodynamic, drug interaction, and toxicity parameters. In 1962, thalidomide treatment in pregnant women led to phocomelia in their newborn but while repurposed based on anti-angiogenesis property, it showed efficacy in hematologic malignancies like multiple myeloma. The repurposing is becoming an essential tool in the anti-cancer drug development due to existing drugs are not effective, high cost of treatment, therapy may degrade the quality of life, improvement of survival after treatment is not guaranteed, relapse may occur, and drug resistance may develop due to tumor heterogeneity. Repurposing can be addressed well with the help of literature-based discovery, high throughput technology, bioinformatics multi-omics approaches, side effects, and phenotypes. Many regulatory bodies like EML, NIH, and FDA promote repurposing programs that support the identification of alternative uses of existing medicines. Cancer becomes the major health issue, and the need to discover promising anti-cancer drugs through repurposing remains very high due to decline in FDA approval since 1990, huge expenses incurred in the drug development and prediction of dangerous future burden.

Download Full-text

Computational Drug Repositioning: Current Progress and Challenges

Applied Sciences ◽

10.3390/app10155076 ◽

2020 ◽

Vol 10 (15) ◽

pp. 5076

Author(s):

Younhee Ko

Keyword(s):

Drug Repositioning ◽

Attrition Rate ◽

Drug Candidates ◽

Disease Mechanisms ◽

High Investment ◽

Experimental Approaches ◽

Approved Drugs ◽

Investment Process ◽

Novel Drug ◽

High Attrition Rate

Novel drug discovery is time-consuming, costly, and a high-investment process due to the high attrition rate. Therefore, many trials are conducted to reuse existing drugs to treat pressing conditions and diseases, since their safety profiles and pharmacokinetics are already available. Drug repositioning is a strategy to identify a new indication of existing or already approved drugs, beyond the scope of their original use. Various computational and experimental approaches to incorporate available resources have been suggested for gaining a better understanding of disease mechanisms and the identification of repurposed drug candidates for personalized pharmacotherapy. In this review, we introduce publicly available databases for drug repositioning and summarize the approaches taken for drug repositioning. We also highlight and compare their characteristics and challenges, which should be addressed for the future realization of drug repositioning.

Download Full-text

Drug Repurposing Techniques in Viral Diseases

10.5772/intechopen.101443 ◽

2021 ◽

Author(s):

Ran Zhang ◽

Rick Oerlemans ◽

Chao Wang ◽

Lili Zhang ◽

Matthew R. Groves

Keyword(s):

Drug Development ◽

Economic Activity ◽

Molecular Mechanisms ◽

Drug Repurposing ◽

Rapid Identification ◽

Drug Candidates ◽

Short Period ◽

Approved Drugs ◽

Antiviral Medications ◽

High Degree

Since the advent of the twentieth century, several severe virus outbreaks have occurred—H1N1 (1918), H2N2 (1957), H3N2 (1968), H1N1 (2009) and recently COVID-19 (2019)—all of which have posed serious challenges to public health. Therefore, rapid identification of efficacious antiviral medications is of ongoing paramount importance in combating such outbreaks. Due to the long cycle of drug development, not only in the development of a “safe” medication but also in mandated and extensive (pre)clinical trials before a drug can be safely licensed for use, it is difficult to access effective and safe novel antivirals. This is of particular importance in addressing infectious disease in appropriately short period of time to limit stress to ever more interlinked societal infrastructures; including interruptions to economic activity, supply routes as well as the immediate impact on health care. Screening approved drugs or drug candidates for antiviral activity to address emergent diseases (i.e. repurposing) provides an elegant and effective strategy to circumvent this problem. As such treatments (in the main) have already received approval for their use in humans, many of their limitations and contraindications are well known, although efficacy against new diseases must be shown in appropriate laboratory trials and clinical studies. A clear in this approach in the case of antivirals is the “relative” simplicity and a high degree of conservation of the molecular mechanisms that support viral replication—which improves the chances for a functional antiviral to inhibit replication in a related viral species. However, recent experiences have shown that while repurposing has the potential to identify such cases, great care must be taken to ensure a rigourous scientific underpinning for repurposing proposals. Here, we present a brief explanation of drug repurposing and its approaches, followed by an overview of recent viral outbreaks and associated drug development. We show how drug repurposing and combination approaches have been used in viral infectious diseases, highlighting successful cases. Special emphasis has been placed on the recent COVID-19 outbreak, and its molecular mechanisms and the role repurposing can/has play(ed) in the discovery of a treatment.

Download Full-text