Liposome-encapsulated aprotinin biodistribution in mice: Side-by-side comparison with free drug formulation.

Injuries of the respiratory system caused by viral infections (e.g., by influenza virus, respiratory syncytial virus, metapneumovirus, or coronavirus) can lead to long-term complications or even life-threatening conditions. The challenges of treatment of such diseases have become particularly pronounced during the recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). One promising drug is the anti-fibrinolytic and anti-inflammatory protease inhibitor aprotinin, which has demonstrated considerable inhibition of the replication of some viruses. Encapsulation of aprotinin in liposomes can significantly improve the effectiveness of the drug, however, the use of nanoparticles as carriers of aprotinin can radically change its biodistribution in the body. Here we show that the liposomal form of aprotinin accumulates more efficiently in the lungs, heart, and kidneys than the molecular form by side-by-side comparison of the ex vivo biodistribution of these two fluorescently labeled formulations in mice using bioimaging. In particular, we synthesized liposomes of different compositions and studied their accumulation in various organs and tissues. Direct comparison of the biodistributions of liposomal and free aprotinin showed that liposomes accumulated in the lungs 1.82 times more effectively, and in the heart and kidneys - 3.56 and 2.00 times, respectively. This suggests that the liposomal formulation exhibits a longer residence time in the target organ and, thus, has the potential for a longer therapeutic effect. The results reveal the great potential of the aprotinin-loaded liposomes for the treatment of respiratory system injuries and heart- and kidney-related complications of viral infections.

Aprotinin-Drug against Respiratory Diseases.

Aprotinin (APR) was discovered in 1930. APR is an effective pan-protease inhibitor, a typical "magic shotgun". Until 2007, APR was widely used as an antithrombotic and anti-inflammatory drug in cardiac and noncardiac surgeries for reduction of bleeding and thus limiting the need for blood transfusion. The ability of APR to inhibit proteolytic activation of some viruses leads to its use as an antiviral drug for the prevention and treatment of acute respiratory virus infections. However, due to incompetent interpretation of several clinical trials followed by incredible controversy in the literature, the usage of APR was nearly stopped for a decade worldwide. In 2015-2020, after re-analysis of these clinical trials' data the restrictions in APR usage were lifted worldwide. This review discusses antiviral mechanisms of APR action and summarizes current knowledge and prospective regarding the use of APR treatment for diseases caused by RNA-containing viruses, including influenza and SARS-CoV-2 viruses, or as a part of combination antiviral treatment.

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.

Synthesis, biological evaluation and in silico modeling of novel pan-genotypic NS5A inhibitors.

A series of novel small-molecule pan-genotypic hepatitis C virus (HCV) NS5A inhibitors with picomolar activity containing 2-[(2S)-pyrrolidin-2-yl]-5-[4-(4-{2-[(2S)-pyrrolidin-2-yl]-1H-imidazol-5-yl}buta-1,3-diyn-1-yl)phenyl]-1H-imidazole core was designed based on molecular modeling study and SAR analysis. The constructed in silico model and docking study provide a deep insight into the binding mode of this type of NS5A inhibitors. Based on the predicted binding interface we have prioritized the most crucial diversity points responsible for improving antiviral activity. The synthesized molecules were tested in a cell-based assay, and compound 1.12 showed an EC50 value in the range of 2.9-34 pM against six genotypes of NS5A HCV, including gT3a, and demonstrated favorable pharmacokinetic profile in rats. This lead compound can be considered as an attractive candidate for further clinical evaluation.

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%.

Safety and efficacy of aviandr in patients with generalized anxiety disorder: A multicenter, randomized, double-blind, placebo-controlled, dose-finding, pilot study.

Generalized anxiety disorder (GAD) is associated with an imbalance in the functioning of the stimulating neurotransmitter systems in human's brain. We studied the safety and therapeutic efficacy of aviandr, the new noradrenergic and specific serotonergic antidepressant, for GAD patients in the phase II, double-blind, placebo-controlled, randomized, multicenter, pilot trial at 17 clinical sites of the Russian Federation. 129 eligible patients were 18 years and older and met the criteria for GAD diagnosis. The patients were randomly assigned (1:1:1) to receive oral aviandr at daily dose of 40 mg (cohort 1, n = 41) or 60 mg (cohort 2, n = 43) or placebo (cohort 3, n = 43) for 8 weeks. The patients were assessed by the Hamilton anxiety scale (HAM-A), Hamilton Depression Scale (HAM-D), Clinical Global Impression Scale (CGI-S), Visual Analogue Scale and vital signs. At week 8, the decreases of the HAM-A score were achieved in 53∙7%, 47∙7% and 16∙3% in cohorts 1, 2 and 3, respectively. Changes of HAM-A, HAM-D, CGI-S, and CGI-I scores in aviandr-treated patients were superior to placebo (p < 0∙001). The psychic components of anxiety decreased on the first day, throughout the 8 weeks of treatment and on a follow-up week after aviandr discontinuation. Aviandr (40 mg daily dose) reduced drowsiness compared to baseline, was safe, well-tolerated and did not cause serious or severe adverse events or signs of withdrawal syndrome within one week after treatment completion. Aviandr at both 40 and 60 mg daily doses demonstrated therapeutic efficacy in GAD patients over placebo.

Synthesis, biological evaluation and in silico modeling of novel integrase strand transfer inhibitors (INSTIs).

Although a relatively wide range of therapeutic options is currently available for the treatment of HIV/AIDS, it is still among the most serious and virulent diseases and is associated with a high mortality rate. Integrase strand transfer inhibitors (INSTIs), e.g., FDA-approved dolutegravir (DTG), bictegravir (BIC) and cabotegravir (CAB), have recently been included in standard highly active antiretroviral therapy (HAART) schemes as one of the five major components responsible for the most beneficial clinical outcome. In this paper, we describe a combinatorial amide synthesis, biological evaluation and in silico modeling of new INSTIs containing heteroaromatic bioisosteric substitution instead of the well-studied halogen-substituted benzyl fragment. With the focus on the mentioned diversity point, a medium-sized library of compounds was selected for synthesis. A biological study revealed that many molecules were highly active INSTIs (EC50 < 10 nM). Two compounds 1{4} and 1{26} demonstrated picomolar antiviral activity that was comparable with CAB and were more active than DTG and BIC. Molecular docking study was performed to evaluate the binding mode of compounds in the active site of HIV-1 IN. In rats, lead compound 1{26} showed two-fold greater bioavailability than CAB and had a similar half-life. Compound 1{26} and its sodium salt were considerably more soluble in water than the parent drugs. Both molecules were very stable in human liver microsomes and plasma, demonstrated high affinity towards plasma proteins and did not show cytochrome (CYP) inhibition. This benefit profile indicates the great potential of these molecules as attractive candidates for subsequent evaluation as oral long-acting drugs and long-acting nanosuspension formulations for intramuscular injection.

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.

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.

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.

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.

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.

Kohonen maps for prediction of binding to human cytochrome P450 3A4.

The drug development process utilizes the parallel assessment of activity at a therapeutic target as well as absorption, distribution, metabolism, excretion, and toxicity properties of molecules. The development of novel, reliable, and inexpensive computational methods for the early assessment of metabolism and toxicity is becoming increasingly an important part of this process. We have used a computational approach for the assessment of drugs and drug-like compounds which bind to the cytochromes P450 (P450s) with experimentally determined Km values. The physicochemical properties of these compounds were calculated using molecular descriptor software and then analyzed using Kohonen self-organizing maps. This approach was applied to generate a P450-specific classification of nearly 500 drug compounds. We observed statistically significant differences in the molecular properties of low Km molecules for various P450s and suggest a relationship between 33 of these compounds and their CYP3A4-inhibitory activity. A test set of additional CYP3A4 inhibitors was used, and 13 of 15 of these molecules were colocated in the regions of low Km values. This computational approach represents a novel method for use in the generation of metabolism models, enabling the scoring of libraries of compounds for their Km values to numerous P450s.