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1.
ChemMedChem ; 17(22): e202200440, 2022 Nov 18.
Article in English | MEDLINE | ID: covidwho-2127643

ABSTRACT

COVID-19, caused by SARS-CoV-2 infection, continues to be a major public health crisis around the globe. Development of vaccines and the first cluster of antiviral drugs has brought promise and hope for prevention and treatment of severe coronavirus disease. However, continued development of newer, safer, and more effective antiviral drugs are critically important to combat COVID-19 and counter the looming pathogenic variants. Studies of the coronavirus life cycle revealed several important biochemical targets for drug development. In the present review, we focus on recent drug design and medicinal chemistry efforts in small molecule drug discovery, including the development of nirmatrelvir that targets viral protein synthesis and remdesivir and molnupiravir that target viral RdRp. These are recent FDA approved drugs for the treatment of COVID-19.


Subject(s)
COVID-19 , Humans , COVID-19/drug therapy , SARS-CoV-2 , Chemistry, Pharmaceutical , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , Antiviral Agents/chemistry , Drug Development
4.
Sci Rep ; 12(1): 13237, 2022 08 02.
Article in English | MEDLINE | ID: covidwho-2016819

ABSTRACT

The identification of novel drug-target interactions (DTI) is critical to drug discovery and drug repurposing to address contemporary medical and public health challenges presented by emergent diseases. Historically, computational methods have framed DTI prediction as a binary classification problem (indicating whether or not a drug physically interacts with a given protein target); however, framing the problem instead as a regression-based prediction of the physiochemical binding affinity is more meaningful. With growing databases of experimentally derived drug-target interactions (e.g. Davis, Binding-DB, and Kiba), deep learning-based DTI predictors can be effectively leveraged to achieve state-of-the-art (SOTA) performance. In this work, we formulated a DTI competition as part of the coursework for a senior undergraduate machine learning course and challenged students to generate component DTI models that might surpass SOTA models and effectively combine these component models as part of a meta-model using the Reciprocal Perspective (RP) multi-view learning framework. Following 6 weeks of concerted effort, 28 student-produced component deep-learning DTI models were leveraged in this work to produce a new SOTA RP-DTI model, denoted the Meta Undergraduate Student DTI (MUSDTI) model. Through a series of experiments we demonstrate that (1) RP can considerably improve SOTA DTI prediction, (2) our new double-cold experimental design is more appropriate for emergent DTI challenges, (3) that our novel MUSDTI meta-model outperforms SOTA models, (4) that RP can improve upon individual models as an ensembling method, and finally, (5) RP can be utilized for low computation transfer learning. This work introduces a number of important revelations for the field of DTI prediction and sequence-based, pairwise prediction in general.


Subject(s)
Drug Development , Drug Discovery , Computer Simulation , Drug Discovery/methods , Drug Interactions , Humans , Machine Learning
5.
Methods Mol Biol ; 2547: 187-199, 2022.
Article in English | MEDLINE | ID: covidwho-2013831

ABSTRACT

The SARS-CoV-2 virus has been the subject of intense pharmacological research. Various pharmacotherapeutic approaches including antiviral and immunotherapy are being explored. A pandemic, however, cannot depend on the development of new drugs; the time required for conventional drug discovery and development is far too lengthy. As such, repurposing drugs is being used as a viable approach for identifying pharmacological agents for COVID-19 infections. Evaluation of repurposed drug candidates with pharmacogenomic analysis is being used to identify near-term pharmacological remedies for COVID-19.


Subject(s)
COVID-19 , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , COVID-19/drug therapy , Drug Development , Drug Repositioning , Humans , Pharmacogenetics , SARS-CoV-2/genetics
6.
Nat Med ; 28(8): 1538, 2022 08.
Article in English | MEDLINE | ID: covidwho-1991644
7.
Pharmacol Rev ; 74(1): 141-206, 2022 01.
Article in English | MEDLINE | ID: covidwho-1978532

ABSTRACT

The number of successful drug development projects has been stagnant for decades despite major breakthroughs in chemistry, molecular biology, and genetics. Unreliable target identification and poor translatability of preclinical models have been identified as major causes of failure. To improve predictions of clinical efficacy and safety, interest has shifted to three-dimensional culture methods in which human cells can retain many physiologically and functionally relevant phenotypes for extended periods of time. Here, we review the state of the art of available organotypic culture techniques and critically review emerging models of human tissues with key importance for pharmacokinetics, pharmacodynamics, and toxicity. In addition, developments in bioprinting and microfluidic multiorgan cultures to emulate systemic drug disposition are summarized. We close by highlighting important trends regarding the fabrication of organotypic culture platforms and the choice of platform material to limit drug absorption and polymer leaching while supporting the phenotypic maintenance of cultured cells and allowing for scalable device fabrication. We conclude that organotypic and microphysiological human tissue models constitute promising systems to promote drug discovery and development by facilitating drug target identification and improving the preclinical evaluation of drug toxicity and pharmacokinetics. There is, however, a critical need for further validation, benchmarking, and consolidation efforts ideally conducted in intersectoral multicenter settings to accelerate acceptance of these novel models as reliable tools for translational pharmacology and toxicology. SIGNIFICANCE STATEMENT: Organotypic and microphysiological culture of human cells has emerged as a promising tool for preclinical drug discovery and development that might be able to narrow the translation gap. This review discusses recent technological and methodological advancements and the use of these systems for hit discovery and the evaluation of toxicity, clearance, and absorption of lead compounds.


Subject(s)
Drug Discovery , Drug-Related Side Effects and Adverse Reactions , Drug Development , Drug Evaluation, Preclinical , Humans , Multicenter Studies as Topic
8.
Am Soc Clin Oncol Educ Book ; 42: 1-8, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1968817

ABSTRACT

Low- and middle-income countries (LMICs) represent a diverse group of regions with varied cancer presentation. Drug development and accessibility across these regions have primarily been dependent on the trials initiated and conducted across high-income countries. Representation of LMIC regions in these trials in terms of study population has been minimal, leading to inequitable distribution of optimal and affordable cancer care. In spite of many challenges, LMICs have now increasingly been able to contribute to anticancer drug development. The opportunities present in LMICs must be explored and used in conjunction with due collaborative efforts from high-income countries, health care planners, and regulatory agencies. Global drug development trials should not only factor in suitable representation of LMICs but also design studies with pragmatic objectives and endpoints so that the trial results lead to equitable and affordable cancer care. Strengthening collaboration between cancer researchers from LMICs and high-income countries and empowering the local investigator with adequate resources will help remove current disparities.


Subject(s)
Drug Development , Neoplasms , Delivery of Health Care , Developing Countries , Humans , Neoplasms/drug therapy , Neoplasms/epidemiology , Poverty
9.
Int J Mol Sci ; 21(16)2020 Aug 06.
Article in English | MEDLINE | ID: covidwho-1934101

ABSTRACT

The recently discovered 340-cavity in influenza neuraminidase (NA) N6 and N7 subtypes has introduced new possibilities for rational structure-based drug design. However, the plasticity of the 340-loop (residues 342-347) and the role of the 340-loop in NA activity and substrate binding have not been deeply exploited. Here, we investigate the mechanism of 340-cavity formation and demonstrate for the first time that seven of nine NA subtypes are able to adopt an open 340-cavity over 1.8 µs total molecular dynamics simulation time. The finding that the 340-loop plays a role in the sialic acid binding pathway suggests that the 340-cavity can function as a druggable pocket. Comparing the open and closed conformations of the 340-loop, the side chain orientation of residue 344 was found to govern the formation of the 340-cavity. Additionally, the conserved calcium ion was found to substantially influence the stability of the 340-loop. Our study provides dynamical evidence supporting the 340-cavity as a druggable hotspot at the atomic level and offers new structural insight in designing antiviral drugs.


Subject(s)
Antiviral Agents/pharmacology , Drug Development , Neuraminidase/chemistry , Orthomyxoviridae/enzymology , Binding Sites , Calcium/chemistry , Ions , Models, Molecular , Molecular Dynamics Simulation , N-Acetylneuraminic Acid/chemistry , Principal Component Analysis , Protein Structure, Secondary , Thermodynamics
10.
Front Cell Infect Microbiol ; 11: 797509, 2021.
Article in English | MEDLINE | ID: covidwho-1924076

ABSTRACT

Malaria, a disease caused by the protozoan parasites Plasmodium spp., is still causing serious problems in endemic regions in the world. Although the WHO recommends artemisinin combination therapies for the treatment of malaria patients, the emergence of artemisinin-resistant parasites has become a serious issue and underscores the need for the development of new antimalarial drugs. On the other hand, new and re-emergences of infectious diseases, such as the influenza pandemic, Ebola virus disease, and COVID-19, are urging the world to develop effective chemotherapeutic agents against the causative viruses, which are not achieved to the desired level yet. In this review article, we describe existing drugs which are active against both Plasmodium spp. and microorganisms including viruses, bacteria, and fungi. We also focus on the current knowledge about the mechanism of actions of these drugs. Our major aims of this article are to describe examples of drugs that kill both Plasmodium parasites and other microbes and to provide valuable information to help find new ideas for developing novel drugs, rather than merely augmenting already existing drug repurposing efforts.


Subject(s)
COVID-19 , Pharmaceutical Preparations , Plasmodium , Drug Development , Humans , Plasmodium falciparum , SARS-CoV-2
11.
J Med Internet Res ; 24(5): e35951, 2022 05 26.
Article in English | MEDLINE | ID: covidwho-1875289

ABSTRACT

The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.


Subject(s)
Delivery of Health Care , Quality of Life , Drug Development , Humans , Information Dissemination
12.
Front Endocrinol (Lausanne) ; 13: 873027, 2022.
Article in English | MEDLINE | ID: covidwho-1862596

ABSTRACT

Thyroid cancer is the most prevalent endocrine malignancy and the reported incidence of thyroid cancer has continued to increase in recent years. Since 2019, coronavirus disease 2019 (COVID-19) has been spreading worldwide in a global pandemic. COVID-19 aggravates primary illnesses and affects disease management; relevant changes include delayed diagnosis and treatment. The thyroid is an endocrine organ that is susceptible to autoimmune attack; thus, thyroid cancer after COVID-19 has gradually attracted attention. Whether COVID-19 affects the diagnosis and treatment of thyroid cancer has also attracted the attention of many researchers. This review examines the literature regarding the influence of COVID-19 on the pathogenesis, diagnosis, and treatment of thyroid cancer; it also focuses on drug therapies to promote research into strategies for improving therapy and management in thyroid cancer patients with COVID-19.


Subject(s)
COVID-19 , Thyroid Neoplasms , COVID-19/drug therapy , Drug Development , Humans , Pandemics , SARS-CoV-2 , Thyroid Neoplasms/drug therapy , Thyroid Neoplasms/epidemiology
13.
J Mol Biol ; 434(6): 167438, 2022 03 30.
Article in English | MEDLINE | ID: covidwho-1851578

ABSTRACT

Recognition of viral infections by various pattern recognition receptors (PRRs) activates an inflammatory cytokine response that inhibits viral replication and orchestrates the activation of adaptive immune responses to control the viral infection. The broadly active innate immune response puts a strong selective pressure on viruses and drives the selection of variants with increased capabilities to subvert the induction and function of antiviral cytokines. This revolutionary process dynamically shapes the host ranges, cell tropism and pathogenesis of viruses. Recent studies on the innate immune responses to the infection of human coronaviruses (HCoV), particularly SARS-CoV-2, revealed that HCoV infections can be sensed by endosomal toll-like receptors and/or cytoplasmic RIG-I-like receptors in various cell types. However, the profiles of inflammatory cytokines and transcriptome response induced by a specific HCoV are usually cell type specific and determined by the virus-specific mechanisms of subverting the induction and function of interferons and inflammatory cytokines as well as the genetic trait of the host genes of innate immune pathways. We review herein the recent literatures on the innate immune responses and their roles in the pathogenesis of HCoV infections with emphasis on the pathobiological roles and therapeutic effects of type I interferons in HCoV infections and their antiviral mechanisms. The knowledge on the mechanism of innate immune control of HCoV infections and viral evasions should facilitate the development of therapeutics for induction of immune resolution of HCoV infections and vaccines for efficient control of COVID-19 pandemics and other HCoV infections.


Subject(s)
Antiviral Agents , Coronavirus Infections , Coronavirus , Drug Development , Immune Evasion , Interferon Type I , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , COVID-19/prevention & control , Coronavirus/immunology , Coronavirus Infections/drug therapy , Coronavirus Infections/immunology , Coronavirus Infections/virology , Humans , Immunity, Innate , Interferon Type I/immunology , Interferon Type I/therapeutic use , SARS-CoV-2/immunology
14.
Molecules ; 27(9)2022 May 06.
Article in English | MEDLINE | ID: covidwho-1847382

ABSTRACT

Drug-target interaction (DTI) prediction through in vitro methods is expensive and time-consuming. On the other hand, computational methods can save time and money while enhancing drug discovery efficiency. Most of the computational methods frame DTI prediction as a binary classification task. One important challenge is that the number of negative interactions in all DTI-related datasets is far greater than the number of positive interactions, leading to the class imbalance problem. As a result, a classifier is trained biased towards the majority class (negative class), whereas the minority class (interacting pairs) is of interest. This class imbalance problem is not widely taken into account in DTI prediction studies, and the few previous studies considering balancing in DTI do not focus on the imbalance issue itself. Additionally, they do not benefit from deep learning models and experimental validation. In this study, we propose a computational framework along with experimental validations to predict drug-target interaction using an ensemble of deep learning models to address the class imbalance problem in the DTI domain. The objective of this paper is to mitigate the bias in the prediction of DTI by focusing on the impact of balancing and maintaining other involved parameters at a constant value. Our analysis shows that the proposed model outperforms unbalanced models with the same architecture trained on the BindingDB both computationally and experimentally. These findings demonstrate the significance of balancing, which reduces the bias towards the negative class and leads to better performance. It is important to note that leaning on computational results without experimentally validating them and by relying solely on AUROC and AUPRC metrics is not credible, particularly when the testing set remains unbalanced.


Subject(s)
Drug Development , Drug Discovery , Drug Development/methods , Drug Discovery/methods , Drug Interactions
15.
Int J Health Serv ; 52(3): 363-371, 2022 07.
Article in English | MEDLINE | ID: covidwho-1846650

ABSTRACT

The process of developing and marketing new pharmaceuticals in the United States is driven by a need to maximize returns to shareholders. This results all too often in the production of new medications that are expensive and of marginal value to patients and society. In line with our heightened awareness of the importance of social justice and public health-and in light of our government's alliance with private companies in bringing us COVID-19 vaccines-we need to reconsider how new pharmaceuticals are developed and distributed. Accordingly, we propose the creation of a new agency of the Food and Drug Administration (FDA) that would direct the whole process. This agency would fund the research and development of high-value medications, closely monitor the clinical studies of these new drugs, and manage their distribution at prices that are value-based, fair, and equitable.


Subject(s)
Drug Development , Drug Industry , United States Food and Drug Administration , COVID-19 Vaccines , Drug Development/legislation & jurisprudence , Drug Development/organization & administration , Humans , Marketing , Pharmaceutical Preparations , United States
16.
J Parkinsons Dis ; 12(4): 1073-1082, 2022.
Article in English | MEDLINE | ID: covidwho-1834290

ABSTRACT

BACKGROUND: As the international community dealt with the ongoing COVID-19 pandemic, important progress continued to be made in the development of new drug-based therapies for the neurodegenerative condition of Parkinson's disease (PD) in 2021. This progress included both "symptomatic treatments" (ST - improves/reduces symptoms of the condition) and "disease modifying treatments" (DMT - attempts to delay/slow progression by addressing the underlying biology of PD), which can be categorised further based on their mechanisms of action and class of drug. OBJECTIVE: This report continues previous efforts to provide an overview of the pharmacological therapies - both ST and DMT - in clinical trials for PD during 2021- 2022, with the aim of creating greater awareness and involvement in the clinical trial process. We also hope to stimulate collaboration amongst all stakeholders, including industry, academia, advocacy organizations, and most importantly patient community. METHODS: We conducted a review of clinical trials of drug therapies for PD using trial data obtained from the ClinicalTrials.gov and World Health Organisation (WHO) registries, and performed a breakdown analysis of studies that were active as of January 31st 2022. We also assessed active drug development projects that had completed one clinical phase but were yet to start the next. RESULTS: There was a total of 147 clinical trials registered on the ClinicalTrials.gov website as active during the period of analysis. Of these trials, 91 (62%)were investigating STs, while 56 (38%)focused on DMTs. Approximately 1/3 of the studies (34.7%; 51 trials) were in Phase 1, while over half of the trials were in Phase 2 (50.3%; 74 trials). Only 15% (22 trials) of the studies were in Phase 3, of which only 3 trials were evaluating DMTs. Novel therapeutics (42%)were the most common type of agents being tested across all phases of testing, followed by repurposed agents (34%)and reformulations (20%). CONCLUSION: Despite significant global health constraints, the development of new drug-based therapies for PD continued in 2021. Hopefully with a shift towards a post-pandemic world in which COVID-19 is better managed, we will see an increase in the number of clinical trials focused on drug development for PD. The need for more Phase 3 studies for DMTs remains acute.


Subject(s)
Drug Development , Parkinson Disease , COVID-19 , Clinical Trials as Topic , Humans , Pandemics , Parkinson Disease/drug therapy
17.
J Ocul Pharmacol Ther ; 37(7): 383-385, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1821676
18.
Comb Chem High Throughput Screen ; 25(4): 634-641, 2022.
Article in English | MEDLINE | ID: covidwho-1817778

ABSTRACT

BACKGROUND: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interactions (DTIs) has been a critical step in the early stages of drug discovery. These computational methods aim to narrow the search space for novel DTIs and to elucidate the functional background of drugs. Most of the methods developed so far use binary classification to predict the presence or absence of interactions between the drug and the target. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If the strength is not strong enough, such a DTI may not be useful. Hence, the development of methods to predict drug-target affinity (DTA) is of significant importance Method: We have improved the GraphDTA model from a dual-channel model to a triple-channel model. We interpreted the target/protein sequences as time series and extracted their features using the LSTM network. For the drug, we considered both the molecular structure and the local chemical background, retaining the four variant networks used in GraphDTA to extract the topological features of the drug and capturing the local chemical background of the atoms in the drug by using BiGRU. Thus, we obtained the latent features of the target and two latent features of the drug. The connection of these three feature vectors is then inputted into a 2 layer FC network, and a valuable binding affinity is the output. RESULT: We used the Davis and Kiba datasets, using 80% of the data for training and 20% of the data for validation. Our model showed better performance when compared with the experimental results of GraphDTA Conclusion: In this paper, we altered the GraphDTA model to predict drug-target affinity. It represents the drug as a graph and extracts the two-dimensional drug information using a graph convolutional neural network. Simultaneously, the drug and protein targets are represented as a word vector, and the convolutional neural network is used to extract the time-series information of the drug and the target. We demonstrate that our improved method has better performance than the original method. In particular, our model has better performance in the evaluation of benchmark databases.


Subject(s)
Drug Development , Neural Networks, Computer , Amino Acid Sequence , Drug Interactions , Molecular Structure
19.
Ther Innov Regul Sci ; 56(6): 964-975, 2022 11.
Article in English | MEDLINE | ID: covidwho-1803265

ABSTRACT

The literature thoroughly describes the challenges of pediatric drug development for rare diseases. This includes (1) generating interest from sponsors, (2) small numbers of children affected by a particular disease, (3) difficulties with study design, (4) lack of definitive outcome measures and assessment tools, (5) the need for additional safeguards for children as a vulnerable population, and (6) logistical hurdles to completing trials, especially with the need for longer term follow-up to establish safety and efficacy. There has also been an increasing awareness of the need to engage patients and their families in drug development processes and to address inequities in access to pediatric clinical trials. The year 2020 ushered in yet another challenge-the COVID-19 pandemic. The pediatric drug development ecosystem continues to evolve to meet these challenges. This article will focus on several key factors including recent regulatory approaches and public health policies to facilitate pediatric rare disease drug development, emerging trends in product development (biologics, molecularly targeted therapies), innovations in trial design/endpoints and data collection, and current efforts to increase patient engagement and promote equity. Finally, lessons learned from COVID-19 about building adaptable pediatric rare disease drug development processes will be discussed.


Subject(s)
Biological Products , COVID-19 , COVID-19/drug therapy , Child , Drug Development , Ecosystem , Humans , Pandemics , Public Health , Rare Diseases/drug therapy
20.
Malar J ; 21(1): 121, 2022 Apr 12.
Article in English | MEDLINE | ID: covidwho-1789122

ABSTRACT

Malaria is one of the most serious infectious diseases affecting predominantly low- and middle-income countries, where pregnant women are among the populations at risk. There are limited options to prevent or treat malaria in pregnancy, particularly in the first trimester, and existing ones may not work optimally in areas where the threat of drug resistance is rising. As malaria elimination is a key goal of the global health community, the inclusion of pregnant women in the adult population to protect from malaria will be key to achieving success. New, safe, and effective options are needed but it can take decades of evidence-gathering before a medicine is recommended for use in pregnancy. This is because pregnant women are typically not included in pre-registration clinical trials due to fear of causing harm. Data to support dosing and safety in pregnancy are subsequently collected in post-licensure studies. There have been growing calls in recent years that this practice needs to change, amplified by the COVID-19 pandemic and increasing public awareness that newly developed medicines generally cannot be administered to pregnant women from the onset. The development of new anti-malarials should ensure that data informing their use in pregnancy and breastfeeding are available earlier. To achieve this, a mindset change and a different approach to medications for pregnant women are needed. Changes in non-clinical, translational, and clinical approaches in the drug development pathway, in line with recent recommendations from the regulatory bodies are proposed in this Comment. The new approach applies to any malaria-endemic region, regardless of the type of Plasmodium responsible for malaria cases. By incorporating intentional and systematic data collection from pre-registration stages of development through post-licensure, it will be possible to inform on the benefit/risk balance of a new anti-malarial earlier and help ensure that the needs of pregnant individuals are addressed in a more timely and equitable manner in the future.


Subject(s)
Antimalarials , COVID-19 , Malaria , Adult , Antimalarials/therapeutic use , Drug Development , Female , Humans , Malaria/drug therapy , Malaria/epidemiology , Malaria/prevention & control , Pandemics , Pregnancy , Pregnant Women
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