Stages Of Drug Development Research Articles

Cocrystal virtual screening based on the XGBoost machine learning model

Co-crystal formation can improve the physicochemical properties of a compound, thus enhancing its druggability. Therefore, artificial intelligence-based co-crystal virtual screening in the early stage of drug development has attracted extensive attention from researchers. However, the complexity of developing and applying algorithms hinders it wide application. This study presents a data-driven co-crystal prediction method based on the XGBoost machine learning model of the scikit-learn package. The simplified molecular input line entry specification (SMILES) information of two compounds is simply inputted to determine whether a co-crystal can be formed. The data set includs the co-crystal records presented in the Cambridge Structural Database (CSD) and the records of no co-crystal formation from extant literature and experiments. RDKit molecular descriptors are adopted as the features of a compound in the data set. The developed model shows excellent performance in the proposed co-crystal training and validation sets with high accuracy, sensitivity, and F1 score. The prediction success rate of the model exceeds 90%. The model therefore provides a simple and feasible scheme for designing and screening co-crystal drugs efficiently and accurately.

Hybrid Approach to Identifying Druglikeness Leading Compounds against COVID-19 3CL Protease.

SARS-CoV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdowns, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In this paper, we developed an in silico study-based hybrid framework to repurpose existing therapeutic agents in finding drug-like bioactive molecules that would cure COVID-19. In the first step, a total of 133 drug-likeness bioactive molecules are retrieved from the ChEMBL database against SARS coronavirus 3CL Protease. Based on the standard IC50, the dataset is divided into three classes: active, inactive, and intermediate. Our comparative analysis demonstrated that the proposed Extra Tree Regressor (ETR)-based QSAR model has improved prediction results related to the bioactivity of chemical compounds as compared to Gradient Boosting-, XGBoost-, Support Vector-, Decision Tree-, and Random Forest-based regressor models. ADMET analysis is carried out to identify thirteen bioactive molecules with the ChEMBL IDs 187460, 190743, 222234, 222628, 222735, 222769, 222840, 222893, 225515, 358279, 363535, 365134, and 426898. These molecules are highly suitable drug candidates for SARS-CoV-2 3CL Protease. In the next step, the efficacy of the bioactive molecules is computed in terms of binding affinity using molecular docking, and then six bioactive molecules are shortlisted, with the ChEMBL IDs 187460, 222769, 225515, 358279, 363535, and 365134. These molecules can be suitable drug candidates for SARS-CoV-2. It is anticipated that the pharmacologist and/or drug manufacturer would further investigate these six molecules to find suitable drug candidates for SARS-CoV-2. They can adopt these promising compounds for their downstream drug development stages.

Drug reprofiling history and potential therapies against Parkinson's disease.

Given the high whittling down rates, high costs, and moderate pace of new medication, revelation, and improvement, repurposing "old" drugs to treat typical and uncommon illnesses is progressively becoming an appealing proposition. Drug repurposing is the way toward utilizing existing medications in treating diseases other than the purposes they were initially designed for. Faced with scientific and economic challenges, the prospect of discovering new medication indications is enticing to the pharmaceutical sector. Medication repurposing can be used at various stages of drug development, although it has shown to be most promising when the drug has previously been tested for safety. We describe strategies of drug repurposing for Parkinson's disease, which is a neurodegenerative condition that primarily affects dopaminergic neurons in the substantia nigra. We also discuss the obstacles faced by the repurposing community and suggest new approaches to solve these challenges so that medicine repurposing can reach its full potential.

Deep learning approaches for <italic>de novo</italic> drug design: an overview

<p indent="0mm">The design and discovery of lead compounds is the most challenging and creative stage in drug development, and multiple attributes of candidate molecules need to be optimized in this process, such as structural novelty, biological activity, target selectivity, synthesability, druggability, and safety. Although the development and application of computer-aided drug design methods have substantially reduced the time and cost in the lead compound discovery stage, it has not yet been able to reverse the current situation of low success rate of drug development. In recent years, with the continuous development of deep learning (DL) technology, DL-based <italic>de novo</italic> drug design methods have brought new opportunities for the discovery of lead compounds. The DL frameworks used by these new drug design models include encoder-decoder, recurrent neural network, generative adversarial networks, and reinforcement learning. In this paper, the basic theory, the input molecular representation and the evaluation metrics of their methods are reviewed, and the application prospect of DL-based <italic>de novo</italic> drug design methods is also discussed.

New Multifunctional Agents for Potential Alzheimer's Disease Treatment Based on Tacrine Conjugates with 2-Arylhydrazinylidene-1,3-Diketones.

Alzheimer's disease (AD) is considered a modern epidemic because of its increasing prevalence worldwide and serious medico-social consequences, including the economic burden of treatment and patient care. The development of new effective therapeutic agents for AD is one of the most urgent and challenging tasks. To address this need, we used an aminoalkylene linker to combine the well-known anticholinesterase drug tacrine with antioxidant 2-tolylhydrazinylidene-1,3-diketones to create 3 groups of hybrid compounds as new multifunctional agents with the potential for AD treatment. Lead compounds of the new conjugates effectively inhibited acetylcholinesterase (AChE, IC50 0.24-0.34 µM) and butyrylcholinesterase (BChE, IC50 0.036-0.0745 µM), with weak inhibition of off-target carboxylesterase. Anti-AChE activity increased with elongation of the alkylene spacer, in agreement with molecular docking, which showed compounds binding to both the catalytic active site and peripheral anionic site (PAS) of AChE, consistent with mixed type reversible inhibition. PAS binding along with effective propidium displacement suggest the potential of the hybrids to block AChE-induced β-amyloid aggregation, a disease-modifying effect. All of the conjugates demonstrated metal chelating ability for Cu2+, Fe2+, and Zn2+, as well as high antiradical activity in the ABTS test. Non-fluorinated hybrid compounds 6 and 7 also showed Fe3+ reducing activity in the FRAP test. Predicted ADMET and physicochemical properties of conjugates indicated good CNS bioavailability and safety parameters acceptable for potential lead compounds at the early stages of anti-AD drug development.

Overview on Strategies and Assays for Antibiotic Discovery.

The increase in antibiotic resistance poses a major threat to global health. Actinomycetes, the Gram-positive bacteria of the order Actinomycetales, are fertile producers of bioactive secondary metabolites, including antibiotics. Nearly two-thirds of antibiotics that are used for the treatment of bacterial infections were originally isolated from actinomycetes strains belonging to the genus Streptomyces. This emphasizes the importance of actinomycetes in antibiotic discovery. However, the identification of a new antimicrobial compound and the exploration of its mode of action are very challenging tasks. Therefore, different approaches that enable the “detection” of an antibiotic and the characterization of the mechanisms leading to the biological activity are indispensable. Beyond bioinformatics tools facilitating the identification of biosynthetic gene clusters (BGCs), whole cell-screenings—in which cells are exposed to actinomycete-derived compounds—are a common strategy applied at the very early stage in antibiotic drug development. More recently, target-based approaches have been established. In this case, the drug candidates were tested for interactions with usually validated targets. This review focuses on the bioactivity-based screening methods and provides the readers with an overview on the most relevant assays for the identification of antibiotic activity and investigation of mechanisms of action. Moreover, the article includes examples of the successful application of these methods and suggestions for improvement.

Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug-drug interaction prediction.

The gold-standard approach for modeling pharmacokinetic mediated drug-drug interactions is the use of physiologically-based pharmacokinetic modeling and population pharmacokinetics. However, these models require extensive amounts of drug-specific data generated from a wide variety of in vitro and in vivo models, which are later refined with clinical data and system-specific parameters. Machine learning has the potential to be utilized for the prediction of drug-drug interactions much earlier in the drug discovery cycle, using inputs derived from, among others, chemical structure. This could lead to refined chemical designs in early drug discovery. Machine-learning models have many advantages, such as the capacity to automate learning (increasing the speed and scalability of predictions), improved generalizability by learning from multicase historical data, and highlighting statistical and potentially clinically significant relationships between input variables. In contrast, the routinely used mechanistic models (physiologically-based pharmacokinetic models and population pharmacokinetics) are currently considered more interpretable, reliable, and require a smaller sample size of data, although insights differ on a case-by-case basis. Therefore, they may be appropriate for later stages of drug-drug interaction assessment when more in vivo and clinical data are available. A combined approach of using mechanistic models to highlight features that can be used for training machine-learning models may also be exploitable in the future to improve the performance of machine learning. In this review, we provide concepts, strategic considerations, and compare machine learning to mechanistic modeling for drug-drug interaction risk assessment across the stages of drug discovery and development.

Therapeutic drug repositioning with special emphasis on neurodegenerative diseases: Threats and issues.

Drug repositioning or repurposing is the process of discovering leading-edge indications for authorized or declined/abandoned molecules for use in different diseases. This approach revitalizes the traditional drug discovery method by revealing new therapeutic applications for existing drugs. There are numerous studies available that highlight the triumph of several drugs as repurposed therapeutics. For example, sildenafil to aspirin, thalidomide to adalimumab, and so on. Millions of people worldwide are affected by neurodegenerative diseases. According to a 2021 report, the Alzheimer’s disease Association estimates that 6.2 million Americans are detected with Alzheimer’s disease. By 2030, approximately 1.2 million people in the United States possibly acquire Parkinson’s disease. Drugs that act on a single molecular target benefit people suffering from neurodegenerative diseases. Current pharmacological approaches, on the other hand, are constrained in their capacity to unquestionably alter the course of the disease and provide patients with inadequate and momentary benefits. Drug repositioning–based approaches appear to be very pertinent, expense- and time-reducing strategies for the enhancement of medicinal opportunities for such diseases in the current era. Kinase inhibitors, for example, which were developed for various oncology indications, demonstrated significant neuroprotective effects in neurodegenerative diseases. This review expounds on the classical and recent examples of drug repositioning at various stages of drug development, with a special focus on neurodegenerative disorders and the aspects of threats and issues viz. the regulatory, scientific, and economic aspects.

Construction of a culture protocol for functional bile canaliculi formation to apply human iPS cell-derived hepatocytes for cholestasis evaluation

Cholestatic toxicity causes the failure of pharmaceutical agents during drug development and, thus, should be identified at an early stage of drug discovery and development. The formation of functional bile canaliculi in human hepatocytes is required for in vitro cholestasis toxicity tests conducted during the early stage of drug development. In this study, we investigated the culture conditions required for the formation of bile canaliculi using human-induced pluripotent stem cell-derived hepatocytes (hiPSC-Heps). When hiPSC-Heps were sandwich-cultured under the condition we established, extended bile canaliculi were formed on the whole well surfaces. Biliary efflux transporters were localized in the formed bile canaliculi structures which had junctional complexes. After the model substrates of the biliary efflux transporters were taken up into cells, their subsequent excretion into the bile canaliculi was observed and was found to be impeded by each inhibitor of the biliary efflux transporter. These findings suggest that bile canaliculi have transporter-specific bile excretion abilities. We will continue to study the application of this culture protocol to cell-based cholestasis assay system. As a result, the culture protocol could lead to a highly predictable, robust cell-based cholestasis assay system because it forms functional bile canaliculi reproducibly and efficiently.

In Silico Prediction of Human and Rat Liver Microsomal Stability via Machine Learning Methods.

Liver microsomal stability is an important property considered for the screening of drug candidates in the early stage of drug development. Determination of hepatic metabolic stability can be performed by an in vitro assay, but it requires quite a few resources and time. In recent years, machine learning methods have made much progress. Therefore, development of computational models to predict liver microsomal stability is highly desirable in the drug discovery process. In this study, the in silico classification models for the prediction of the metabolic stability of compounds in rat and human liver microsomes were constructed by the conventional machine learning and deep learning methods. The performance of the models was evaluated using the test and external sets. For the rat liver microsomes (RLM) stability, the best model yielded the AUC values of 0.84 and 0.71 on the test and external validation sets, respectively. For the human liver microsome (HLM) stability, the best model exhibited the AUC values of 0.86 and 0.77 on the test and external validation sets, respectively. In addition, several important substructure fragments were detected using information gain and frequency substructure analysis methods. The applicability domain of the models was defined using the Euclidean distance-based method. We anticipate that our results would be helpful for the prediction of liver microsomal stability of compounds in the early stage of drug discovery.

Pros and cons of NRF2 activation as adjunctive therapy in rheumatoid arthritis.

Rheumatoid arthritis (RA) is an autoimmune disease with an important inflammatory component accompanied by deregulated redox-dependent signaling pathways that are feeding back into inflammation. In this context, we bring into focus the transcription factor NRF2, a master redox regulator that exerts exquisite antioxidant and anti-inflammatory effects. The review does not intend to be exhaustive, but to point out arguments sustaining the rationale for applying an NRF2-directed co-treatment in RA as well as its potential limitations. The involvement of NRF2 in RA is emphasized through an analysis of publicly available transcriptomic data on NRF2 target genes and the findings from NRF2-knockout mice. The impact of NRF2 on concurrent pathologic mechanisms in RA is explained by its crosstalk with major redox-sensitive inflammatory and cell death-related pathways, in the context of the increased survival of pathologic cells in RA. The proposed adjunctive therapy targeted to NRF2 is further sustained by the existence of promising NRF2 activators that are in various stages of drug development. The interference of NRF2 with conventional anti-rheumatic therapies is discussed, including the cytoprotective effects of NRF2 for alleviating drug toxicity. From another perspective, the review presents how NRF2 activation would be decreasing the efficacy of synthetic anti-rheumatic drugs by increasing drug efflux. Future perspectives regarding pharmacologic NRF2 activation in RA are finally proposed.

S-25-03 Multiscale models, integrating several data types, applied to predict clinical pharmacological adverse events in early stages of drug development

S-25-03 Multiscale models, integrating several data types, applied to predict clinical pharmacological adverse events in early stages of drug development

Replacement in angiogenesis research: Studying mechanisms of blood vessel development by animal-free in vitro, in vivo and in silico approaches.

Angiogenesis, the development of new blood vessels from pre-existing ones, is an essential process determining numerous physiological and pathological conditions. Accordingly, there is a high demand for research approaches allowing the investigation of angiogenic mechanisms and the assessment of pro- and anti-angiogenic therapeutics. The present review provides a selective overview and critical discussion of such approaches, which, in line with the 3R principle, all share the common feature that they are not based on animal experiments. They include in vitro assays to study the viability, proliferation, migration, tube formation and sprouting activity of endothelial cells in two- and three-dimensional environments, the degradation of extracellular matrix compounds as well as the impact of hemodynamic forces on blood vessel formation. These assays can be complemented by in vivo analyses of microvascular network formation in the chorioallantoic membrane assay and early stages of zebrafish larvae. In addition, the combination of experimental data and physical laws enables the mathematical modeling of tissue-specific vascularization, blood flow patterns, interstitial fluid flow as well as oxygen, nutrient and drug distribution. All these animal-free approaches markedly contribute to an improved understanding of fundamental biological mechanisms underlying angiogenesis. Hence, they do not only represent essential tools in basic science but also in early stages of drug development. Moreover, their advancement bears the great potential to analyze angiogenesis in all its complexity and, thus, to make animal experiments superfluous in the future.

The Relevance of Crystal Forms in the Pharmaceutical Field: Sword of Damocles or Innovation Tools?

This review is aimed to provide to an “educated but non-expert” readership and an overview of the scientific, commercial, and ethical importance of investigating the crystalline forms (polymorphs, hydrates, and co-crystals) of active pharmaceutical ingredients (API). The existence of multiple crystal forms of an API is relevant not only for the selection of the best solid material to carry through the various stages of drug development, including the choice of dosage and of excipients suitable for drug development and marketing, but also in terms of intellectual property protection and/or extension. This is because the physico-chemical properties, such as solubility, dissolution rate, thermal stability, processability, etc., of the solid API may depend, sometimes dramatically, on the crystal form, with important implications on the drug’s ultimate efficacy. This review will recount how the scientific community and the pharmaceutical industry learned from the catastrophic consequences of the appearance of new, more stable, and unsuspected crystal forms. The relevant aspects of hydrates, the most common pharmaceutical solid solvates, and of co-crystals, the association of two or more solid components in the same crystalline materials, will also be discussed. Examples will be provided of how to tackle multiple crystal forms with screening protocols and theoretical approaches, and ultimately how to turn into discovery and innovation the purposed preparation of new crystalline forms of an API.

Spray drying and nano spray drying as manufacturing methods of drug-loaded polymeric particles.

In this review, benefits and drawbacks of the process of spray drying and nano spray drying with regard to the manufacturing of polymeric particles for pharmaceutical applications are discussed. Spray drying has been used for many years in the food, chemical and pharmaceutical industries for converting liquids into solids, in order to form products of uniform appearance. The construction of spray dryer enables to atomize the liquid into small droplets, which ensures a large surface area for heat and mass transfer, and significantly shortens the processing. Each droplet dries to an individual solid microparticle of characteristic features that can be tailored by optimizing formulation variables and critical process parameters. Since spray drying technology is easy to scale up and can be used for drying almost any drug in a solution or suspension, there are numerous examples of products in clinical use, in which this process has been successfully applied to improve drug stability, enhance bioavailability or control its release rate. In recent years, nano spray drying technology has been proposed as a method for lab-scale manufacturing of nanoparticles. Such an approach is of particular interest at early stages of drug development, when a small amount of new chemical entities is available. Here, the nebulization technique is used for feed atomization, while laminar gas flow in the drying chamber ensures gentle drying conditions. Moreover, electrostatic collectors have gradually replaced cyclone separators, ensuring high effectiveness in producing solid nanoparticles, even if a small volume of the sample is processed.

Current practices for QSP model assessment: an IQ consortium survey.

Quantitative Systems Pharmacology (QSP) modeling is increasingly applied in the pharmaceutical industry to influence decision making across a wide range of stages from early discovery to clinical development to post-marketing activities. Development of standards for how these models are constructed, assessed, and communicated is of active interest to the modeling community and regulators but is complicated by the wide variability in the structures and intended uses of the underlying models and the diverse expertise of QSP modelers. With this in mind, the IQ Consortium conducted a survey across the pharmaceutical/biotech industry to understand current practices for QSP modeling. This article presents the survey results and provides insights into current practices and methods used by QSP practitioners based on model type and the intended use at various stages of drug development. The survey also highlights key areas for future development including better integration with statistical methods, standardization of approaches towards virtual populations, and increased use of QSP models for late-stage clinical development and regulatory submissions.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10928-022-09811-1.

Matching within a hybrid RCT/RWD: framework on associated causal estimands

ABSTRACT As the regulatory environment becomes progressively receptive toward utilizing real-world evidence, a spectrum of real-world data incorporation techniques in trial conduct and analysis has seen increasing interest and adoption in different stages of drug development. Of particular interest is leveraging external control data to augment the control arm in a concurrent randomized controlled trial, where patients are enrolled in both investigational treatment arm and the control arm. Yet despite the emerging literature in external data borrowing in a hybrid trial setting, very little discussion focuses on delineating what should be matched and what is actually being estimated, especially when a variety of matching schemes can be considered. In general, external control can be matched in four different ways: (1) matching with the intersection between investigational treatment and concurrent control, (2) matching with the union of concurrent investigational treatment and concurrent control, (3) matching with concurrent control alone, and (4) matching with investigational treatment alone. In this article, the formulation of estimands for different matching schemes are detailed to describe what these matching methods facilitate to answer. Simulation studies are also conducted to evaluate the performance characteristics under different matching schemes, estimation methods, effect size assumptions, and missingness of confounders.

The Efficacy and Safety of Cystic Fibrosis Gene Therapy Clinical Trials: A Systematic Review and Meta-Analysis

BackgroundGene therapy has been proposed as a treatment approach for cystic fibrosis by replacing the single defective gene, cystic fibrosis transmembrane regulator (CFTR), through topical lung delivery. Relatively few studies have addressed gene therapy for cystic fibrosis.Objectives Via referral to the published literature, this study aimed to identify any success of the gene therapy approach for cystic fibrosis regarding experimental and routine clinical outcomes in different drug development stages, and to determine any adverse effects noted. MethodologyA search of the PubMed database (NCBI) for 1989–2020 was made using predefined selection criteria for clinical trials on patients with cystic fibrosis receiving viral and non-viral lung delivery systems of the CFTR gene. Several features in the reviewed studies were examined, including clinical phase (1–3), sample size, delivery target cells/vector, and reported adverse effects. A quantitative estimate of treatment intervention success was evaluated using a meta-analysis approach.Results A total of 20 studies with 549 patients were included in the review. The studies involved the delivery of the defective gene to the lung, nasal mucosa, and sinuses, and were mainly phase 1–2, randomized controlled trials; there were no phase three studies. The vector for gene transfer was liposome or viral. % predicted FEV1 was statistically significant between intervention and control patients in two trials. Gene transfer was detected to a higher degree in intervention patients than control; this outcome measure was assessed using bronchoscopy assessment of vector-specific DNA and mRNA expression in lung and nasal mucosa. These effects, however, were temporary. The safety of the gene therapy approach was confirmed. ConclusionReportedly, the gene therapy approach is safe but has limited and temporary efficacy. Newer approaches should thus be engineered to deliver the necessary genetic material with the desired, full-scale efficacy.

CSatDTA: Prediction of Drug-Target Binding Affinity Using Convolution Model with Self-Attention.

Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial. The proposed model, the prediction of drug–target affinity using a convolution model with self-attention (CSatDTA), applies convolution-based self-attention mechanisms to the molecular drug and target sequences to predict drug–target affinity (DTA) effectively, unlike previous convolution methods, which exhibit significant limitations related to this aspect. The convolutional neural network (CNN) only works on a particular region of information, excluding comprehensive details. Self-attention, on the other hand, is a relatively recent technique for capturing long-range interactions that has been used primarily in sequence modeling tasks. The results of comparative experiments show that CSatDTA surpasses previous sequence-based or other approaches and has outstanding retention abilities.

Pharmacokinetic/Pharmacodynamic Modeling and Application in Antibacterial and Antifungal Pharmacotherapy: A Narrative Review.

Pharmacokinetics and pharmacodynamics are areas in pharmacology related to different themes in the pharmaceutical sciences, including therapeutic drug monitoring and different stages of drug development. Although the knowledge of these disciplines is essential, they have historically been treated separately. While pharmacokinetics was limited to describing the time course of plasma concentrations after administering a drug-dose, pharmacodynamics describes the intensity of the response to these concentrations. In the last decades, the concept of pharmacokinetic/pharmacodynamic modeling (PK/PD) emerged, which seeks to establish mathematical models to describe the complete time course of the dose-response relationship. The integration of these two fields has had applications in optimizing dose regimens in treating antibacterial and antifungals. The anti-infective PK/PD models predict the relationship between different dosing regimens and their pharmacological activity. The reviewed studies show that PK/PD modeling is an essential and efficient tool for a better understanding of the pharmacological activity of antibacterial and antifungal agents.