The Efficacy of Aprotinin Combinations with Selected Antiviral Drugs in Mouse Models of Influenza Pneumonia and Coronavirus Infection Caused by SARS-CoV-2.

The efficacy of aprotinin combinations with selected antiviral-drugs treatment of influenza virus and coronavirus (SARS-CoV-2) infection was studied in mice models of influenza pneumonia and COVID-19. The high efficacy of the combinations in reducing virus titer in lungs and body weight loss and in increasing the survival rate were demonstrated. This preclinical study can be considered a confirmatory step before introducing the combinations into clinical assessment.

AVIFAVIR for Treatment of Patients With Moderate Coronavirus Disease 2019 (COVID-19): Interim Results of a Phase II/III Multicenter Randomized Clinical Trial.

In May 2020 the Russian Ministry of Health granted fast-track marketing authorization to RNA polymerase inhibitor AVIFAVIR (favipiravir) for the treatment of COVID-19 patients. In the pilot stage of Phase II/III clinical trial, AVIFAVIR enabled SARS-CoV-2 viral clearance in 62.5% of patients within 4 days, and was safe and well-tolerated. Clinical Trials Registration. NCT04434248.

Effect of Aprotinin and Avifavir® Combination Therapy for Moderate COVID-19 Patients.

COVID-19 is a contagious multisystem inflammatory disease caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We studied the efficacy of Aprotinin (nonspecific serine proteases inhibitor) in combination with Avifavir® or Hydroxychloroquine (HCQ) drugs, which are recommended by the Russian Ministry of Health for the treatment therapy of moderate COVID-19 patients. This prospective single-center study included participants with moderate COVID-19-related pneumonia, laboratory-confirmed SARS-CoV-2, and admitted to the hospitals. Patients received combinations of intravenous (IV) Aprotinin (1,000,000 KIU daily, 3 days) and HCQ (cohort 1), inhalation (inh) treatment with Aprotinin (625 KIU four times per day, 5 days) and HCQ (cohort 2) or IV Aprotinin (1,000,000 KIU daily for 5 days) and Avifavir (cohort 3). In cohorts 1-3, the combination therapy showed 100% efficacy in preventing the transfer of patients (n = 30) to the intensive care unit (ICU). The effect of the combination therapy in cohort 3 was the most prominent, and the median time to SARS-CoV-2 elimination was 3.5 days (IQR 3.0-4.0), normalization of the CRP concentration was 3.5 days (IQR 3-5), of the D-dimer concentration was 5 days (IQR 4 to 5); body temperature was 1 day (IQR 1-3), improvement in clinical status or discharge from the hospital was 5 days (IQR 5-5), and improvement in lung lesions of patients on 14 day was 100%.

A Selective Ligand for Estrogen Receptor Proteins Discriminates Rapid and Genomic Signaling.

Estrogen exerts extensive and diverse effects throughout the body of women. In addition to the classical nuclear estrogen receptors (ERα and ERß), the G protein-coupled estrogen receptor GPER is an important mediator of estrogen action. Existing ER-targeted therapeutic agents act as GPER agonists. Here, we report the identification of a small molecule, named AB-1, with the previously unidentified activity of high selectivity for binding classical ERs over GPER. AB-1 also possesses a unique functional activity profile as an agonist of transcriptional activity but an antagonist of rapid signaling through ERα. Our results define a class of small molecules that discriminate between the classical ERs and GPER, as well as between modes of signaling within the classical ERs. Such an activity profile, if developed into an ER antagonist, could represent an opportunity for the development of first-in-class nuclear hormone receptor-targeted therapeutics for breast cancer exhibiting reduced acquired and de novo resistance.

In silico approaches to prediction of aqueous and DMSO solubility of drug-like compounds: trends, problems and solutions.

The solubility of drugs and drug-like compounds has been the subject of extensive studies aimed at finding a way to predict solubility from molecular structure. The aqueous solubility of a drug is an important factor that influences its absorption, distribution and elimination in the body. Poor aqueous solubility often causes a drug to appear inactive and may cause other biological problems. Compound solubility in DMSO represents another serious problem in early stages of drug discovery. An appreciation of the factors affecting a compound's DMSO solubility could help in predicting the storage conditions and appropriateness of compounds for primary bioscreening programs. In silico procedures for estimation of water and DMSO solubility represent extremely useful tools for the drug discovery practitioners. In this review, we provide a critical discussion of in silico models for the prediction of DMSO and water solubility of drug-like compounds used for virtual screening. We describe the main tendencies in the field, "booming" approaches and unsolved problems. A critical analysis of the accuracy and applicability of methods is provided.

Recent progress in development of non-ATP competitive small-molecule inhibitors of protein kinases.

The majority of marketed and late stage development kinase inhibitors are reported to be ATP-competitive. As a result, many promising drug candidates display non-specific activity that results in undesired physiological effects. There is growing interest towards non-ATP competitive kinase inhibitors, as they are expected to yield highly specific and efficacious molecules devoid of non-mechanistic toxicity. Recent developments in this area are summarized in our review.

Rational design approaches to chemical libraries for hit identification.

Sequencing of the human genome along with developments in combinatorial synthesis and high-throughput biological screening provide unparallel opportunities to drug discovery. It has been noted that the increased number of synthesized and annotated compounds did not yield the expected increase in number of viable drug candidates. To address this problem, several novel computation technologies have emerged for making combinatorial library design cost-effective. Of particular interest for the modern drug discovery are the structure-based or target-based methods that use structural information about target proteins and their small molecule ligands. In this work, we provide an overview of selected advances in computational algorithms for the rational selection of molecule libraries for the synthesis, with emphasis on structure-based approaches. These include a fusion of scaffold-linking method and combinatorial library design, pharmacophore matching and informative library design, and search by 3-D tree topological descriptors.

Exploring the chemogenomic knowledge space with annotated chemical libraries.

The recent human genome initiatives have led to the discovery of a multitude of genes that are potentially associated with various pathologic conditions and, thus, have opened new horizons in drug discovery. Simultaneously, annotated chemical libraries have emerged as information-rich databases to integrate biological and chemical data. They can be useful for the discovery of new pharmaceutical leads, the validation of new biotargets and the determination of the structural basis of ligand selectivity within target families. Annotated libraries provide a strong information basis for computational design of target-directed combinatorial libraries, which are a key component of modern drug discovery. Today, the rational design of chemical libraries enhanced with chemogenomics data is a new area of progressive research.

Comprehensive computational assessment of ADME properties using mapping techniques.

One strategy to potentially improve the success of drug discovery is to apply computational approaches early in the process to select molecules and scaffolds with ideal binding and physicochemical properties. Numerous algorithms and different molecular descriptors have been used for modeling ligand-protein interactions as well as absorption, distribution, metabolism and excretion (ADME) properties. In most cases a single data set has been evaluated with one approach or multiple algorithms that have been compared for a single dataset. These models have been primarily evaluated by leave-one out analysis or boot strapping with groups representing 25-50% of the training set left out of the final model. In a very few examples a test set of molecules not included in the model has been used for an external evaluation. In the present study we have applied Sammon non-linear maps, Support Vector Machines and Kohonen Self Organizing Maps to modeling numerous datasets for ADME properties including human intestinal absorption, blood brain barrier permeability, cytochrome P450 binding, plasma protein binding, P-gp inhibition, volume of distribution and plasma half life.

Modeling of human cytochrome p450-mediated drug metabolism using unsupervised machine learning approach.

We developed a computational algorithm for evaluating the possibility of cytochrome P450-mediated metabolic transformations that xenobiotics molecules undergo in the human body. First, we compiled a database of known human cytochrome P-450 substrates, products, and nonsubstrates for 38 enzyme-specific groups (total of 2200 compounds). Second, we determined the cytochrome-mediated metabolic reactions most typical for each group and examined the substrates and products of these reactions. To assess the probability of P450 transformations of novel compounds, we built a nonlinear quantitative structure-metabolism relationships (QSMR) model based on Kohonen self-organizing maps (SOM). This neural network QSMR model incorporated a predefined set of physicochemical descriptors encoding the key molecular properties that define the metabolic fate of individual molecules. Isozyme-specific groups of substrate molecules were visualized, thus facilitating prediction of tissue-specific metabolism. The developed algorithm can be used in early stages of drug discovery as an efficient tool for the assessment of human metabolism and toxicity of novel compounds in designing discovery libraries and in lead optimization.

In silico estimation of DMSO solubility of organic compounds for bioscreening.

Solubility of organic compounds in DMSO is an important issue for commercial and academic organizations handling large compound collections or performing biological screening. In particular, solubility data are critical for the optimization of storage conditions and for the selection of compounds for bioscreening compatible with the assay protocol. Solubility is largely determined by the solvation energy and the crystal disruption energy, and these molecular phenomena should be assessed in structure-solubility correlation studies. The authors summarize our long-term experimental observations and theoretical studies of physicochemical determinants of DMSO solubility of organic substances. They compiled a comprehensive reference database of proprietary data on compound solubility (55,277 compounds with good DMSO solubility and 10,223 compounds with poor DMSO solubility), calculated specific molecular descriptors (topological, electromagnetic, charge, and lipophilicity parameters), and applied an advanced machine-learning approach for training neural networks to address the solubility. Both supervised (feed-forward, back-propagated neural networks) and unsupervised (Kohonen neural networks) learning methods were used. The resulting neural network models were validated by successfully predicting DMSO solubility of compounds in independent test selections.

Efficient optimization strategy for marginal hits active against abl tyrosine kinases.

Primary high-throughput screening of commercially available small molecules collections often results in hit compounds with unfavorable ADME/Tox properties and low IP potential. These issues are addressed empirically at follow-up lead development and optimization stages. In this work, we describe a rational approach to the optimization of hit compounds discovered during screening of a kinase focused library against abl tyrosine kinase. The optimization strategy involved application of modern chemoinformatics techniques, such as automatic bioisosteric transformation of the initial hits, efficient solution-phase combinatorial synthesis, and advanced methods of knowledge-based libraries design.

Computational mapping tools for drug discovery.

During the past decade, computational technologies have become well integrated in the modern drug design process and have gained in influence. They have dramatically revolutionized the way in which we approach drug discovery, leading to the explosive growth in the amount of chemical and biological data that are typically multidimensional in structure. As a result, the irresistible rush towards using computational approaches has focused on dimensionality reduction and the convenient representation of high-dimensional data sets. This has, in turn, led to the development of advanced machine-learning algorithms. In this review we describe a variety of conceptually different mapping techniques that have attracted the attention of researchers because they allow analysis of complex multidimensional data in an intuitively comprehensible visual manner.

Structure-based versus property-based approaches in the design of G-protein-coupled receptor-targeted libraries.

In this work, two alternative approaches to the design of small-molecule libraries targeted for several G-protein-coupled receptor (GPCR) classes were explored. The first approach relies on the selection of structural analogues of known active compounds using a substructural similarity method. The second approach, based on an artificial neural network classification procedure, searches for compounds that possess physicochemical properties typical of the GPCR-specific agents. As a reference base, 3365 GPCR-active agents belonging to nine different GPCR classes were used. General rules were developed which enabled us to assess possible areas where both approaches would be useful. The predictability of the neural network algorithm based on 14 physicochemical descriptors was found to exceed the predictability of the similarity-based approach. The structural diversity of high-scored subsets obtained with the neural network-based method exceeded the diversity obtained with the similarity-based approach. In addition, the descriptor distributions of the compounds selected by the neural network algorithm more closely approximate the corresponding distributions of the real, active compounds than did those selected using the alternative method.

Classification scheme for the design of serine protease targeted compound libraries.

The development of a scoring scheme for the classification of molecules into serine protease (SP) actives and inactives is described. The method employed a set of pre-selected descriptors for encoding the molecular structures, and a trained neural network for classifying the molecules. The molecular requirements were profiled and validated by using available databases of SP- and non-SP-active agents [1,439 diverse SP-active molecules, and 5,131 diverse non-SP-active molecules from the Ensemble Database (Prous Science, 2002)] and Sensitivity Analysis. The method enables an efficient qualification or disqualification of a molecule as a potential serine protease ligand. It represents a useful tool for constraining the size of virtual libraries that will help accelerate the development of new serine protease active drugs.

Property-based design of GPCR-targeted library.

The design of a GPCR-targeted library, based on a scoring scheme for the classification of molecules into "GPCR-ligand-like" and "non-GPCR-ligand-like", is outlined. The methodology is a valuable tool that can aid in the selection and prioritization of potential GPCR ligands for bioscreening from large collections of compounds. It is based on the distillation of knowledge from large databases of GPCR and non-GPCR active agents. The method employed a set of descriptors for encoding the molecular structures and by training of a neural network for classifying the molecules. The molecular requirements were profiled and validated by using available databases of GPCR- and non-GPCR-active agents [5736 diverse GPCR-active molecules and 7506 diverse non-GPCR-active molecules from the Ensemble Database (Prous Science, 2002)]. The method enables efficient qualification or disqualification of a molecule as a potential GPCR ligand and represents a useful tool for constraining the size of GPCR-targeted libraries that will help speed up the development of new GPCR-active drugs.

Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions.

Support Vector Machines (SVM) is a powerful classification and regression tool that is becoming increasingly popular in various machine learning applications. We tested the ability of SVM, in comparison with well-known neural network techniques, to predict drug-likeness and agrochemical-likeness for large compound collections. For both kinds of data, SVM outperforms various neural networks using the same set of descriptors. We also used SVM for estimating the activity of Carbonic Anhydrase II (CA II) enzyme inhibitors and found that the prediction quality of our SVM model is better than that reported earlier for conventional QSAR. Model characteristics and data set features were studied in detail.

Advanced exact structure searching in large databases of chemical compounds.

Efficient recognition of tautomeric compound forms in large corporate or commercially available compound databases is a difficult and labor intensive task. Our data indicate that up to 0.5% of commercially available compound collections for bioscreening contain tautomers. Though in the large registry databases, such as Beilstein and CAS, the tautomers are found in an automated fashion using high-performance computational technologies, their real-time recognition in the nonregistry corporate databases, as a rule, remains problematic. We have developed an effective algorithm for tautomer searching based on the proprietary chemoinformatics platform. This algorithm reduces the compound to a canonical structure. This feature enables rapid, automated computer searching of most of the known tautomeric transformations that occur in databases of organic compounds. Another useful extension of this methodology is related to the ability to effectively search for different forms of compounds that contain ionic and semipolar bonds. The computations are performed in the Windows environment on a standard personal computer, a very useful feature. The practical application of the proposed methodology is illustrated by several examples of successful recovery of tautomers and different forms of ionic compounds from real commercially available nonregistry databases.

Quantitative structure-metabolism relationship modeling of metabolic N-dealkylation reaction rates.

It is widely recognized that preclinical drug discovery can be improved via the parallel assessment of bioactivity, absorption, distribution, metabolism, excretion, and toxicity properties of molecules. High-throughput computational methods may enable such assessment at the earliest, least expensive discovery stages, such as during screening compound libraries and the hit-to-lead process. As an attempt to predict drug metabolism and toxicity, we have developed an approach for evaluation of the rate of N-dealkylation mediated by two of the most important human cytochrome P450s (P450), namely CYP3A4 and CYP2D6. We have taken a novel approach by using descriptors generated for the whole molecule, the reaction centroid, and the leaving group, and then applying neural network computations and sensitivity analysis to generate quantitative structure-metabolism relationship models. The quality of these models was assessed by using the cross-validated correlation coefficients of 0.82 for CYP3A4 and 0.79 for CYP2D6 as well as external test molecules for each enzyme. The relative performance of different neural networks was also compared, and modular neural networks with two hidden layers provided the best predictive ability. Functional dependencies between the neural network input and output variables, generalization ability, and limitations of the described approach are also discussed. These models represent an initial approach to predicting the rate of P450-mediated metabolism and may be applied and integrated with other models for P450 binding to produce a systems-based approach for predicting drug metabolism.