Stages Of Drug Design Research Articles

Actionable Predictions of Human Pharmacokinetics at the Drug Design Stage.

We present a novel computational approach for predicting human pharmacokinetics (PK) that addresses the challenges of early stage drug design. Our study introduces and describes a large-scale data set of 11 clinical PK end points, encompassing over 2700 unique chemical structures to train machine learning models. To that end multiple advanced training strategies are compared, including the integration of in vitro data and a novel self-supervised pretraining task. In addition to the predictions, our final model provides meaningful epistemic uncertainties for every data point. This allows us to successfully identify regions of exceptional predictive performance, with an absolute average fold error (AAFE/geometric mean fold error) of less than 2.5 across multiple end points. Together, these advancements represent a significant leap toward actionable PK predictions, which can be utilized early on in the drug design process to expedite development and reduce reliance on nonclinical studies.

In Silico Analysis for Exploring the Potential Inhibitors Against Breast Cancer (MCF7) Using Curcumin Analogue Compounds

Breast cancer is one of the most problematic diseases in the world. Currently, there are no potential vaccines for the treatment of this disease. Therefore, finding effective compounds, such as curcumin analogs, is crucial to inhibit breast cancer. Forty-five synthesized curcumin analogs were tested on the MCF7 cell line using the MTT assay. It was shown that nine curcumin compounds, Cpd 5, Cpd 9, Cpd 17, Cpd 18, Cpd 21, Cpd 25, Cpd 28, Cpd 32, and Cpd 45, had better inhibitory activities against breast cancer. Furthermore, in silico analysis was developed using 2D and 3D QSAR models with high predictive ability, with an r2 value of 0.834. In addition, based on molecular docking, molecular dynamics, and pharmacophore results, it was shown that these nine compounds had the lowest binding free energy and were also stable during the simulation. The presence of methoxy groups, hydrogen bond donors, and aromatic ring features are the main factors that enhance the biological activity of curcumin analogs. Therefore, these compounds could serve as references for the next stage of drug design.

ADME Studies of Phytochemicals in Stachys pilifera Benth plant species

Medicinal chemistry is a constantly changing and developing field. It is a successful and prosperous branch due to the new technologies, the changing complexities of medicine, and the expansion of medicinal chemistry knowledge. In drug design, many aspects need to be checked. The first step in drug development is discovering when new insights and technologies become available. Discovery is possible to find new treatments for diseases. This can begin with thousands of different computational methods, including under Computer-aided drug design (CADD). It provides a no. of tools and techniques that assist the various stages of drug design which can minimize the cost of drug research and development time. For this particular project, we use the SwissADME web tool it is a free web tool, easily assessable and convenient. It can characterize any chemical compound within a min. it allows you to compute physiochemical descriptors also to predict ADME parameters, pharmacokinetic properties, drug like nature of our compound, and medicinal chemistry friendliness of many small molecules to support drug discovery. For ADME analysis we chose a plant in which the isolation of its chemical compound is already done. i.e. stachys pilifera benth plant species. We did the ADME study of their chemical compounds using SwissADME and concluded its drug-like nature, its bioavailability, its synthetic accessibility, and many medicinal properties. We can say that Swiss ADME is the Asset for medicinal chemistry.

Характеристика модели асцитной карциномы Эрлиха и перспективы ее применения в экспериментальной фармакологии ветеринарных препаратов

The article provides an overview of the most significant works of domestic and foreign authors devoted to the Ehrlich ascites carcinoma biological model. The analysis of the available literary sources showed that despite the long history of the existence of this model and its application in biomedical research, it has not yet become widespread in veterinary medicine. It should be noted that the Ehrlich ascites carcinoma model has a number of peculiarities that allow it to be used for the needs of veterinary medicine, in particular, experimental pharmacology at the stage of drug design and its preclinical testing. The high resistance of malignant cells and tumor survival in experimental animals, as well as the ease of its cultivation and relatively rapid tumor growth, make this model a convenient biological object for research. The effect of tumor cells on the body of the animal with a tumor leads to a change in a number of biochemical indicators of various tissues and organs, and a general toxic effect. In this case, an imbalance of the free-radical and antioxidant systems is observed, the body is exposed to oxidative stress. The immune system under the effect of Ehrlich ascites carcinoma also undergoes a number of changes. Thus, in response to the development of tumor-induced pathological processes, recruitment of immunocompetent cells and an antitumor immune response are observed, which at the same time is suppressed as carcinoma cells grow under the action of immunosuppressive physiologically active compounds produced by the tumor, escape of tumor cells from immunological surveillance and remodelling of immune cells in the tumor microenvironment. These processes lead to suppression of the immune system and as a result to the death of animals. These peculiarities make it possible to use the Ehrlich transplantable ascites carcinoma model for testing new veterinary drugs with potential antiblastoma, immunomodulatory, antitoxic properties, as well as medicinal formulations for restoring biochemical indicators and redox homeostasis under conditions of tumor-induced immunosuppression and chronic inflammatory process, and also to study the mechanisms of cell adaptation, including the immune system, under the action of carcinogenesis factors.

Muc2-дефицитные мыши как модель хронического воспаления

The article provides an overview of the most significant works of domestic and foreign authors devoted to the Ehrlich ascites carcinoma biological model. The analysis of the available literary sources showed that despite the long history of the existence of this model and its application in biomedical research, it has not yet become widespread in veterinary medicine. It should be noted that the Ehrlich ascites carcinoma model has a number of peculiarities that allow it to be used for the needs of veterinary medicine, in particular, experimental pharmacology at the stage of drug design and its preclinical testing. The high resistance of malignant cells and tumor survival in experimental animals, as well as the ease of its cultivation and relatively rapid tumor growth, make this model a convenient biological object for research. The effect of tumor cells on the body of the animal with a tumor leads to a change in a number of biochemical indicators of various tissues and organs, and a general toxic effect. In this case, an imbalance of the free-radical and antioxidant systems is observed, the body is exposed to oxidative stress. The immune system under the effect of Ehrlich ascites carcinoma also undergoes a number of changes. Thus, in response to the development of tumor-induced pathological processes, recruitment of immunocompetent cells and an antitumor immune response are observed, which at the same time is suppressed as carcinoma cells grow under the action of immunosuppressive physiologically active compounds produced by the tumor, escape of tumor cells from immunological surveillance and remodelling of immune cells in the tumor microenvironment. These processes lead to suppression of the immune system and as a result to the death of animals. These peculiarities make it possible to use the Ehrlich transplantable ascites carcinoma model for testing new veterinary drugs with potential antiblastoma, immunomodulatory, antitoxic properties, as well as medicinal formulations for restoring biochemical indicators and redox homeostasis under conditions of tumor-induced immunosuppression and chronic inflammatory process, and also to study the mechanisms of cell adaptation, including the immune system, under the action of carcinogenesis factors.

Computational Predictions of Nonclinical Pharmacokinetics at the Drug Design Stage.

Although computational predictions of pharmacokinetics (PK) are desirable at the drug design stage, existing approaches are often limited by prediction accuracy and human interpretability. Using a discovery data set of mouse and rat PK studies at Roche (9,685 unique compounds), we performed a proof-of-concept study to predict key PK properties from chemical structure alone, including plasma clearance (CLp), volume of distribution at steady-state (Vss), and oral bioavailability (F). Ten machine learning (ML) models were evaluated, including Single-Task, Multitask, and transfer learning approaches (i.e., pretraining with in vitro data). In addition to prediction accuracy, we emphasized human interpretability of outcomes, especially the quantification of uncertainty, applicability domains, and explanations of predictions in terms of molecular features. Results show that intravenous (IV) PK properties (CLp and Vss) can be predicted with good precision (average absolute fold error, AAFE of 1.96-2.84 depending on data split) and low bias (average fold error, AFE of 0.98-1.36), with AutoGluon, Gaussian Process Regressor (GP), and ChemProp displaying the best performance. Driven by higher complexity of oral PK studies, predictions of F were more challenging, with the best AAFE values of 2.35-2.60 and higher overprediction bias (AFE of 1.45-1.62). Multi-Task approaches and pretraining of ChemProp neural networks with in vitro data showed similar precision to Single-Task models but helped reduce the bias and increase correlations between observations and predictions. A combination of GP-computed prediction variance, molecular clustering, and dimensionality-reduction provided valuable quantitative insights into prediction uncertainty and applicability domains. SHAPley Additive exPlanations (SHAPs) highlighted molecular features contributing to prediction outcomes of Vss, providing explanations that could aid drug design. Combined results show that computational predictions of PK are feasible at the drug design stage, with several ML technologies converging to successfully leverage historical PK data sets. Further studies are needed to unlock the full potential of this approach, especially with respect to data set sizes and quality, transfer learning between in vitro and in vivo data sets, model-independent quantification of uncertainty, and explainability of predictions.

The New Era of Drug Discovery: The Power of Computer-aided Drug Design (CADD)

Abstract: Drug design and discovery is a process that requires high financial costs and is timeconsuming. For many years, this process focused on empirical pharmacology. However, over the years, the target-based approach allowed a significant discovery in this field, initiating the rational design era. In view, to decrease the time and financial cost, rational drug design is benefited by increasing computer engineering and software development, and computer-aided drug design (CADD) emerges as a promising alternative. Since the 1970s, this approach has been able to identify many important and revolutionary compounds, like protease inhibitors, antibiotics, and others. Many anticancer compounds identified through this approach have shown their importance, being CADD essential in any drug discovery campaign. Thus, this perspective will present the prominent successful cases utilizing this approach and entering into the next stage of drug design. We believe that drug discovery will follow the progress in bioinformatics, using high-performance computing with molecular dynamics protocols faster and more effectively. In addition, artificial intelligence and machine learning will be the next process in the rational design of new drugs. Here, we hope that this paper generates new ideas and instigates research groups worldwide to use these methods and stimulate progress in drug design.

Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds.

While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are complex and often unfavorable. To predict clinical PK in early drug discovery, we built human physiologically based PK (PBPK) models integrating either (i) machine learning (ML)-predicted properties or (ii) discovery stage in vitro data. Our test set was composed of 12 challenging development compounds with high lipophilicity (mean calculated log P 4.2), low plasma-free fraction (50% of compounds with fu,p < 1%), and low aqueous solubility. Predictions focused on key human PK parameters, including plasma clearance (CL), volume of distribution at steady state (Vss), and oral bioavailability (%F). For predictions of CL, the ML inputs showed acceptable accuracy and slight underprediction bias [an average absolute fold error (AAFE) of 3.55; an average fold error (AFE) of 0.95]. Surprisingly, use of measured data only slightly improved accuracy but introduced an overprediction bias (AAFE = 3.35; AFE = 2.63). Predictions of Vss were more successful, with both ML (AAFE = 2.21; AFE = 0.90) and in vitro (AAFE = 2.24; AFE = 1.72) inputs showing good accuracy and moderate bias. The %F was poorly predicted using ML inputs [average absolute prediction error (AAPE) of 45%], and use of measured data for solubility and permeability improved this to 34%. Sensitivity analysis showed that predictions of CL limited the overall accuracy of human PK predictions, partly due to high nonspecific binding of lipophilic compounds, leading to uncertainty of unbound clearance. For accurate predictions of %F, solubility was the key factor. Despite current limitations, this work encourages further development of ML models and integration of their results within PBPK models to enable human PK prediction at the drug design stage, even before compounds are synthesized. Further evaluation of this approach with more diverse chemical types is warranted.

An In Silico Explainable Multiparameter Optimization Approach for De Novo Drug Design against Proteins from the Central Nervous System.

The aim of drug design and development is to produce a drug that can inhibit the target protein and possess a balanced physicochemical and toxicity profile. Traditionally, this is a multistep process where different parameters such as activity and physicochemical and pharmacokinetic properties are optimized sequentially, which often leads to high attrition rate during later stages of drug design and development. We have developed a deep learning-based de novo drug design method that can design novel small molecules by optimizing target specificity as well as multiple parameters (including late-stage parameters) in a single step. All possible combinations of parameters were optimized to understand the effect of each parameter over the other parameters. An explainable predictive model was used to identify the molecular fragments responsible for the property being optimized. The proposed method was applied against the human 5-hydroxy tryptamine receptor 1B (5-HT1B), a protein from the central nervous system (CNS). Various physicochemical properties specific to CNS drugs were considered along with the target specificity and blood-brain barrier permeability (BBBP), which act as an additional challenge for CNS drug delivery. The contribution of each parameter toward molecule design was identified by analyzing the properties of generated small molecules from optimization of all possible parameter combinations. The final optimized generative model was able to design similar inhibitors compared to known inhibitors of 5-HT1B. In addition, the functional groups of the generated small molecules that guide the BBBP predictive model were identified through feature attribution techniques.

Comparison between lab variability and in silico prediction errors for the unbound fraction of drugs in human plasma

Variability of the unbound fraction in plasma (fu) between labs, methods and conditions is known to exist. Variability and uncertainty of this parameter influence predictions of the overall pharmacokinetics of drug candidates and might jeopardise safety in early clinical trials. Objectives of this study were to evaluate the variability of human in vitro fu-estimates between labs for a range of different drugs, and to develop and validate an in silico fu-prediction method and compare the results to the lab variability. A new in silico method with prediction accuracy (Q2) of 0.69 for log fu was developed. The median and maximum prediction errors were 1.9- and 92-fold, respectively. Corresponding estimates for lab variability (ratio between max and min fu for each compound) were 2.0- and 185-fold, respectively. Greater than 10-fold lab variability was found for 14 of 117 selected compounds. Comparisons demonstrate that in silico predictions were about as reliable as lab estimates when these have been generated during different conditions. Results propose that the new validated in silico prediction method is valuable not only for predictions at the drug design stage, but also for reducing uncertainties of fu-estimations and improving safety of drug candidates entering the clinical phase.

Drug discovery, explain how lead compound delivers to the target site and claim if there are recommended antiviral drugs to COVID-19.

Drug design is a long and costly process taking many stages; start with target identification passing through target validation, lead identification, and candidate optimization of pre-clinical and clinical trials. Drug design types depend on screening of a large number of molecules to distinguish and can select the most effective drug with high pharmaceutical effect. Ligand and Structure Based Drug Design are the two types of drug design. The drug takes journey when administered into the body through several ways (oral, inhalation, Intravenous (IV), Intramuscular (IM)) to deliver its target site. Twenty years ago, Computational strategies applied to understand particular target molecules with hits achieving lead target and that helps in lead Identification and Optimization stages of drug design and development. Screening, molecular modification and rational drug design are the three approaches to search for a modern drug. Bioinformatics plays a vital role in the discovery of drug as Bioinformatics includes both the programmed preparing of huge amounts of existing information and the creation of modern sorts of data resource. Both required in case the information is to be changed into data and utilized to assist in drug discovery. This work will discuss what is the meaning of drug design? What are the stages of drug design? What are the drug design types? Computer- Aided Drug Design (CAAD), Approaches in the search for a new drug, What is the journey of drug to deliver its target site? What are the methods of drug delivery? And will discuss the recommended antiviral therapy in the management of COVID-19.

Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage.

Breast Cancer Resistance Protein (BCRP) is an ATP-dependent efflux transporter linked to the multidrug resistance phenomenon in many diseases such as epilepsy and cancer and a potential source of drug interactions. For these reasons, the early identification of substrates and nonsubstrates of this transporter during the drug discovery stage is of great interest. We have developed a computational nonlinear model ensemble based on conformational independent molecular descriptors using a combined strategy of genetic algorithms, J48 decision tree classifiers, and data fusion. The best model ensemble consists in averaging the ranking of the 12 decision trees that showed the best performance on the training set, which also demonstrated a good performance for the test set. It was experimentally validated using the ex vivo everted rat intestinal sac model. Five anticonvulsant drugs classified as nonsubstrates for BRCP by the model ensemble were experimentally evaluated, and none of them proved to be a BCRP substrate under the experimental conditions used, thus confirming the predictive ability of the model ensemble. The model ensemble reported here is a potentially valuable tool to be used as an in silico ADME filter in computer-aided drug discovery campaigns intended to overcome BCRP-mediated multidrug resistance issues and to prevent drug-drug interactions.

Computational Screening and Design for Compounds that Disrupt Protein-protein Interactions.

Protein-protein interactions play key roles in all biological processes, motivating numerous campaigns to seek small-molecule disruptors of therapeutically relevant interactions. Two decades ago, the prospect of developing small-molecule inhibitors was thought to be perhaps impossible due to the potentially undruggable nature of the protein surfaces involved; this viewpoint was reinforced by the limited successes provided from traditional high-throughput screens. To date, however, refinement of new experimental approaches has led to a multitude of inhibitors against many different targets. Having thus established the feasibility of attaining success in this valuable and diverse target space, attention now turns to incorporating computational techniques that might assist during various stages of drug design and optimization. Here we review cases in which computational approaches - virtual screening, docking, and ligand optimization - have contributed to discovery of new inhibitors of protein-protein interactions. We conclude by providing an outlook into the upcoming challenges and recent advances likely to shape this field moving forward.

Thermodynamic Approaches to the Challenges of Solubility in Drug Discovery and Development

This paper considers fundamental aspects determining the processes of drug compound dissolution and distribution in solvents/systems modeling biological media. Special attention is paid to the complex analysis of thermodynamic functions of sublimation and solvation/hydration processes during structural modification of lead/hit compounds and their influence on dissolution processes. The paper shows that at the first stages of drug design it is necessary to develop algorithms of optimizing the properties determining absorption, distribution, metabolism, and excretion (ADME) characteristics of molecules: in particular, diffusion flux density through lipophilic membranes. Using sulfonamides and nonsteroidal anti-inflammatory drugs (NSAIDs) as an example, we have demonstrated the efficiency of approaches to manipulating thermodynamic functions of the basic processes that result from delivering compounds to the points of their operation. We have formulated several criteria of compound selection for further tests.

Fusion of nonclinical and clinical data to predict human drug safety

Adverse drug reactions continue to be a major cause of morbidity in both patients receiving therapeutics and in drug R&D programs. Predicting and possibly eliminating these adverse events remains a high priority in industry, government agencies and healthcare systems. With small molecule candidates, the fusion of nonclinical and clinical data is essential in establishing an overall system that creates a true translational science approach. Several new advances are taking place that attempt to create a ‘patient context’ mechanism early in drug research and development and ultimately into the marketplace. This ‘life-cycle’ approach has as its core the development of human-oriented, nonclinical end points and the incorporation of clinical knowledge at the drug design stage. The next 5 years should witness an explosion of what the author views as druggable and safe chemical space, pharmacosafety molecular targets and the most important aspect, an understanding of unique susceptibilities in patients developing adverse drug reactions. Our current knowledge of clinical safety relies completely on pharmacovigilance data from approved and marketed drugs, with a few exceptions of drugs failing in clinical trials. Massive data repositories now and soon to be available via cloud computing should stimulate a major effort in expanding our view of clinical drug safety and its incorporation into early drug research and development.

Assessing Drug Transport Across the Human Placental Barrier: From In Vivo and In Vitro Measurements to the Ex Vivo Perfusion Method and In silico Techniques

Assessing drug transport across the human placental barrier is of vital importance in order to guarantee drug safety during pregnancy. However, due to ethical reasons, in vivo fetal development risk assessment studies related to maternal drugs and chemicals exposure remain extremely limited. To overcome any ethical issues, several in vitro models applying primary trophoblastic cells, immortal cell lines and tissue explants of placental origin have recently been advanced. Alternatively, ex vivo human placental perfusion seems to be a more representative and highly informative method, which offers better insights into the different drug transporters, xenobiotic metabolism and tissue binding. Recently, in silico techniques have further been advanced as complementary tools to validate experimental placental transfer data, offering an attractive alternative for high throughput screening of potential fetotoxicity at the early stages of drug design. The present review scrutinizes, from a critical point of view, the current trends and perspectives in the emerging topic of drug transport across the human placental barrier. The special characteristics of the recently developed biopharmaceuticals on the transplacental transfer process are also discussed.

ChemInform Abstract: Structural Features of Toxic Chemicals for Specific Toxicity.

We have studied the structural features of toxic chemicals from the RTECS database associated with specific toxicity. The frameworks, functional groups, and structure patterns of molecules are taken into account. Potential active frameworks, groups, and structure patterns for specific toxicity are gained by computational chemistry approaches. These structural features of toxic chemicals will be helpful in understanding activities of toxic chemicals and useful in predicting the toxicity of chemicals, especially in the early stage of drug design.

The use of quantum chemistry in the prediction of ADME-Tox properties

ADME-Tox properties are very important in pharmaceutical research, determining the fate of many molecules in the drug design sequence. Knowledge of ADME-Tox properties in the earliest stages of drug design is therefore highly desirable. The aim of this investigation is to construct low throughput in silico QSAR models in which ADME-Tox properties of single compounds are predicted with high accuracy based on Quantum Chemical information [1]. The possible role of quantum chemical information in chemoinformatics is discussed, with a closer look to the advantages, disadvantages and capabilities of quantum chemical descriptors in QSAR environments. The use of quantum chemical information is explained by a worked-out example concerning the distribution of medicinal active molecules through the blood-brain barrier [2].

Neue klinische Technologien in der Arzneimittelentwicklung

The integration of functional imaging and therapeutic nuclear medicine technologies mainly in the early phases of drug development (clinical technologies implementation, CTI) is a relatively young option, which has amply influenced drug development concepts in the pharmaceutical industry. Over the past 15 years, the greatest risk reduction in drug development decision making has resulted from the earlier involvement of drug metabolism in the optimization stage of drug design rather than the late-stage characterization of target molecules. Go/no-go decisions of drug development programs and drug dosage can also be inferred from these techniques, which lead to decreases in time and costs. Not only is CTI effectively used in early drug development, but also in later clinical study phases (phases 3b/4) as a therapy monitoring or follow-up instrument. In addition, CTI is interesting and promising for the development of new therapy concepts in nuclear medicine which involve the application of monoclonal antibodies and peptides for treatment of different hematologic-oncologic diseases.

Structure-based ligand design for flexible proteins: application of new F-DycoBlock.

A method of structure-based ligand design - DycoBlock - has been proposed and tested by Liu et al. It was further improved by Zhu et al. and applied to design new selective inhibitors of cyclooxygenase 2. In the current work, we present a new methodology - F-DycoBlock that allows for the incorporation of receptor flexibility. During the designing procedure, both the receptor and molecular building blocks are subjected to the multiple-copy stochastic molecular dynamics (MCSMD) simulation, while the protein moves in the mean field of all copies. It is tested for two enzymes studied previously - cyclooxygenase 2 (COX-2) and human immunodeficiency type 1 (HIV-1) protease. To identify the applicability of F-DycoBlock, the binding protein structure was used as starting point to explore the conformational space around the bound state. This method can be easily extended to accommodate the flexibility in different degree. Four types of treatment of the receptor flexibility - all-atom restrained, backbone restrained, intramolecular hydrogen-bond restrained and active-site flexible - were tested with or without the grid approximation. Two inhibitors, SC-558 for COX-2 and L700417 for HIV-1 protease, are used in this testing study for comparison with previous results. The accuracy of recovery, binding energy, solvent accessible surface area (SASA) and positional root-mean-square (RMS) deviation are used as criteria. The results indicate that F-DycoBlock is a robust methodology for flexible drug design. It is particularly notable that the protein flexibility has been perfectly associated with each stage of drug design - search for the binding sites, dynamic assembly and optimization of candidate compounds. When all protein atoms were restrained, F-DycoBlock yielded higher accuracy of recovery than DycoBlock (100%). If backbone atoms were restrained, the same ratio of accuracy was achieved. Moreover, with the intramolecular hydrogen bonds restrained, reasonable conformational changes were observed for HIV- 1 protease during the long-time MCSMD simulation and L700417 was reassembled at the active site. It makes it possible to study the receptor motion in the binding process.