Serotonin transporter: Recent progress of in silico ligand prediction methods and structural biology towards structure-guided in silico design of therapeutic agents.

Serotonin transporter (SERT) is a membrane transporter which terminates neurotransmission of serotonin through its reuptake. This transporter as well as its substrate have long drawn attention as a key mediator and drug target in a variety of diseases including mental disorders. Accordingly, its structural basis has been studied by X-ray crystallography to gain insights into a design of ligand with high affinity and high specificity over closely related transporters. Recent progress in structural biology including single particle cryo-EM have made big strides also in determination of the structures of human SERT in complex with its ligands. Moreover, rapid progress in machine learning such as deep learning accelerates computer-assisted drug design. Here, we would like to summarize recent progresses in our understanding of SERT using these two rapidly growing technologies, limitations, and future perspectives.

Newly Emerging Strategies in Antiviral Drug Discovery: Dedicated to Prof. Dr. Erik De Clercq on Occasion of His 80th Anniversary.

Viral infections pose a persistent threat to human health. The relentless epidemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global health problem, with millions of infections and fatalities so far. Traditional approaches such as random screening and optimization of lead compounds by organic synthesis have become extremely resource- and time-consuming. Various modern innovative methods or integrated paradigms are now being applied to drug discovery for significant resistance in order to simplify the drug process. This review provides an overview of newly emerging antiviral strategies, including proteolysis targeting chimera (PROTAC), ribonuclease targeting chimera (RIBOTAC), targeted covalent inhibitors, topology-matching design and antiviral drug delivery system. This article is dedicated to Prof. Dr. Erik De Clercq, an internationally renowned expert in the antiviral drug research field, on the occasion of his 80th anniversary.

Structural biology of SARS-CoV-2: open the door for novel therapies.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the causative agent of the pandemic disease COVID-19, which is so far without efficacious treatment. The discovery of therapy reagents for treating COVID-19 are urgently needed, and the structures of the potential drug-target proteins in the viral life cycle are particularly important. SARS-CoV-2, a member of the Orthocoronavirinae subfamily containing the largest RNA genome, encodes 29 proteins including nonstructural, structural and accessory proteins which are involved in viral adsorption, entry and uncoating, nucleic acid replication and transcription, assembly and release, etc. These proteins individually act as a partner of the replication machinery or involved in forming the complexes with host cellular factors to participate in the essential physiological activities. This review summarizes the representative structures and typically potential therapy agents that target SARS-CoV-2 or some critical proteins for viral pathogenesis, providing insights into the mechanisms underlying viral infection, prevention of infection, and treatment. Indeed, these studies open the door for COVID therapies, leading to ways to prevent and treat COVID-19, especially, treatment of the disease caused by the viral variants are imperative.

Artificial intelligence-enhanced drug design and development: Toward a computational precision medicine.

Artificial Intelligence (AI) relies upon a convergence of technologies with further synergies with life science technologies to capture the value of massive multi-modal data in the form of predictive models supporting decision-making. AI and machine learning (ML) enhance drug design and development by improving our understanding of disease heterogeneity, identifying dysregulated molecular pathways and therapeutic targets, designing and optimizing drug candidates, as well as evaluating in silico clinical efficacy. By providing an unprecedented level of knowledge on both patient specificities and drug candidate properties, AI is fostering the emergence of a computational precision medicine allowing the design of therapies or preventive measures tailored to the singularities of individual patients in terms of their physiology, disease features, and exposure to environmental risks.

New approaches to the design of analgesic medicinal substances.

A gamma-pyrone derivative, comenic acid, activates the opioid-like receptor-mediated signaling pathway that modulates the NaV1.8 channels in the primary sensory neuron membrane. These channels are responsible for the generation of the nociceptive signal; therefore, gamma-pyrones have great therapeutic potential as analgesics, and this effect deserves a deeper understanding. The novelty of our approach to the design of a medicinal substance is based on a combination of the data obtained from living neurons using very sensitive physiological methods and the results of quantum chemical calculations. This approach allows the correlation of the molecular structure of gamma-pyrones with their ability to evoke a physiological response of the neuron. Comenic acid can bind to two calcium cations. One of them is chelated by the carbonyl and hydroxyl functional groups, while the other forms a salt bond with the carboxylate anion. Calcium-bound gamma-pyrones have fundamentally different electrostatic properties from free gamma-pyrone molecules. These two calcium ions are key elements involved in ligand-receptor binding. It is very likely that ion-ionic interactions between these cations and anionic functional groups of the opioid-like receptor activate the latter. The calculated intercationic distance of 9.5 Å is a structural criterion for effective ligand-receptor binding of calcium-bound gamma-pyrones.

Identification of Influenza PAN Endonuclease Inhibitors via 3D-QSAR Modeling and Docking-Based Virtual Screening.

Structural and biochemical studies elucidate that PAN may contribute to the host protein shutdown observed during influenza A infection. Thus, inhibition of the endonuclease activity of viral RdRP is an attractive approach for novel antiviral therapy. In order to envisage structurally diverse novel compounds with better efficacy as PAN endonuclease inhibitors, a ligand-based-pharmacophore model was developed using 3D-QSAR pharmacophore generation (HypoGen algorithm) methodology in Discovery Studio. As the training set, 25 compounds were taken to generate a significant pharmacophore model. The selected pharmacophore Hypo1 was further validated by 12 compounds in the test set and was used as a query model for further screening of 1916 compounds containing 71 HIV-1 integrase inhibitors, 37 antibacterial inhibitors, 131 antiviral inhibitors and other 1677 approved drugs by the FDA. Then, six compounds (Hit01-Hit06) with estimated activity values less than 10 µM were subjected to ADMET study and toxicity assessment. Only one potential inhibitory 'hit' molecule (Hit01, raltegravir's derivative) was further scrutinized by molecular docking analysis on the active site of PAN endonuclease (PDB ID: 6E6W). Hit01 was utilized for designing novel potential PAN endonuclease inhibitors through lead optimization, and then compounds were screened by pharmacophore Hypo1 and docking studies. Six raltegravir's derivatives with significant estimated activity values and docking scores were obtained. Further, these results certainly do not confirm or indicate the seven compounds (Hit01, Hit07, Hit08, Hit09, Hit10, Hit11 and Hit12) have antiviral activity, and extensive wet-laboratory experimentation is needed to transmute these compounds into clinical drugs.

[Advances in Pharmaceutical Manufacturing Process Management -From Physical Pharmaceutics to Automatic Pharmaceutical Production].

Since entering graduate school 43 years ago, I have been studying physical pharmaceutics with a focus on the effects of environmental factors on pharmaceutical properties of solid oral dosage forms during the manufacturing process. I have reported on changes in the characteristics of pharmaceutical products during manufacturing processes, such as grinding, mixing, granulation, and tableting owing to complicated phenomena based on chemical reactions or the crystalline polymorphic transitions of bulk drugs and excipients. To develop modern pharmaceutical manufacturing processes based on process analysis technology (PAT) as a next generation good manufacturing practice, real-time monitoring was introduced in these processes using a non-destructive analytical method, such as the near-infrared spectroscopy combined with chemometrics. Many case studies related to the mixing, granulation, tableting, and coating processes involving PAT have been reported. In those studies, I focused on clarifying the physical and chemical mechanism through "design space" representation. Additionally, non-destructive analytical methods, including X-ray computed tomography, audible acoustic emission, Raman spectroscopy, terahertz spectroscopy, and infrared thermal imaging analysis were applied as novel candidate analytical methods to the pharmaceutical process to monitor critical quality attributes. To achieve this purpose in various pharmaceutical dosage forms, I have been attempting the assembly of a modern manufacturing process managed through a "design space" paradigm involving in-line monitoring using novel analytical methods, multivariate analyses, and feed-back systems.

Advances with antibody-drug conjugates in breast cancer treatment.

Antibody-drug conjugate-based therapy for treatment of cancer has attracted much attention because of its enhanced efficacy against numerous cancer types. Commonly, an ADC includes a mAb linked to a therapeutic payload. Antibody, linker and payload are the three main components of ADCs. The high specificity of antibodies is integrated with the strong potency of payloads in ADCs. ADCs with potential cytotoxic small molecules as payloads, generate antibody-mediated cancer therapy. Recently, ADCs with DNA-damaging agents have shown favor over microtubule-targeting agents as payloads. Although ADC resistance can be a barrier to effectiveness, several ADC therapies have been either approved or are in clinical trials for cancer treatment. The ADC-based treatments of breast cancers, particularly TNBC, MDR and metastatic breast cancers, have shown promise in recent years. This review discusses ADC drug designs, and developed for different types of breast cancer including TNBC, MDR and metastatic breast cancer.

Quality by design tools reducing the gap from bench to bedside for nanomedicine.

Pharmaceutical nanotechnology research is focused on smart nano-vehicles, which can deliver active pharmaceutical ingredients to enhance their efficacy through any route of administration and in the most varied therapeutical application. The design and development of new nanopharmaceuticals can be very laborious. In recent years, the application of mathematics, statistics and computational tools is emerging as a convenient strategy for this purpose. The application of Quality by Design (QbD) tools has been introduced to guarantee quality for pharmaceutical products and improve translational research from the laboratory bench into applicable therapeutics. In this review, a collection of basic-concept, historical overview and application of QbD in nanomedicine are discussed. A specific focus has been put on Response Surface Methodology and Artificial Neural Network approaches in general terms and their application in the development of nanomedicine to monitor the process parameters obtaining optimized system ensuring its quality profile.

[Formulation Development for the Purpose of Improving Medication Adherence].

Medication adherence is an important factor in the success or failure of drug treatment. No matter how good a drug is, if a patient cannot or does not want to take it, the therapeutic effect of the drug will not be sufficient and as expected. Therefore, we have been developing formulations with "clinical functionality", namely, formulation characteristics that enhance the likelihood of obtaining the expected therapeutic effect. We researched formulations that are easy to take and deliver expected results; these formulations include gummy drugs as confectionery-like formulations and orally disintegrating (OD) tablets that can be easily swallowed. In particular, OD tablets have been jointly developed with pharmaceutical companies and have been commercialized. Clinical trials with gummy drugs and OD tablets have been conducted to verify the impact of these formulations with clinical functionality on improving medication adherence.

Drugging the Undruggable: How Isoquinolines and PKA Initiated the Era of Designed Protein Kinase Inhibitor Therapeutics.

In 1984, Japanese researchers led by the biochemist Hiroyoshi Hidaka described the first synthetic protein kinase inhibitors based on an isoquinoline sulfonamide structure (Hidaka et al. Biochemistry, 1984 Oct 9; 23(21): 5036-41. doi: 10.1021/bi00316a032). These led to the first protein kinase inhibitor approved for medical use (fasudil), an inhibitor of the AGC subfamily Rho kinase. With potencies strong enough to compete against endogenous ATP, the isoquinoline compounds established the druggability of the ATP binding site. Crystal structures of their protein kinase complexes, including with cAMP-dependent protein kinase (PKA), showed interactions that, on the one hand, could mimic ATP but, on the other hand, could be optimized for high potency binding, kinase selectivity, and diversification away from adenosine. They also showed the flexibility of the glycine-rich loop, and PKA became a major prototype for crystallographic and nuclear magnetic resonance (NMR) studies of protein kinase mechanism and dynamic activity control. Since fasudil, more than 70 kinase inhibitors have been approved for clinical use, involving efforts that progressively have introduced new paradigms of data-driven drug discovery. Publicly available data alone comprise over 5000 protein kinase crystal structures and hundreds of thousands of binding data. Now, new methods, including artificial intelligence techniques and expansion of protein kinase targeting approaches, together with the expiration of patent protection for optimized inhibitor scaffolds, promise even greater advances in drug discovery. Looking back to the time of the first isoquinoline hinge binders brings the current state-of-the-art into stark contrast. Appropriately for this Perspective article, many of the milestone papers during this time were published in Biochemistry (now ACS Biochemistry).

Ensembles in solution as a new paradigm for antibody structure prediction and design.

The rise of antibodies as a promising and rapidly growing class of biotherapeutic proteins has motivated numerous studies to characterize and understand antibody structures. In the past decades, the number of antibody crystal structures increased substantially, which revolutionized the atomistic understanding of antibody functions. Even though numerous static structures are known, various biophysical properties of antibodies (i.e., specificity, hydrophobicity and stability) are governed by their dynamic character. Additionally, the importance of high-quality structures in structure-function relationship studies has substantially increased. These structure-function relationship studies have also created a demand for precise homology models of antibody structures, which allow rational antibody design and engineering when no crystal structure is available. Here, we discuss various aspects and challenges in antibody design and extend the paradigm of describing antibodies with only a single static structure to characterizing them as dynamic ensembles in solution.

Drug discovery for enzymes.

Enzymes are essential, physiological catalysts involved in all processes of life, including metabolism, cellular signaling and motility, as well as cell growth and division. They are attractive drug targets because of the presence of defined substrate-binding pockets, which can be exploited as binding sites for pharmaceutical enzyme inhibitors. Understanding the reaction mechanisms of enzymes and the molecular mode of action of enzyme inhibitors is indispensable for the discovery and development of potent, efficacious, and safe novel drugs. The combination of classical concepts of enzymology with new experimental and data analysis methods opens new routes for drug discovery.

Minimising hypoglycaemia in the real world: the challenge of insulin.

Insulin therapy has been a life saver for people with type 1 diabetes and has been an essential tool in the therapy of people with type 2 diabetes, but the risk for hypoglycaemia has been a major hurdle to achieving good glycaemic control for most. Insulin analogues, the availability of novel technologies for the administration of insulin, like insulin pumps, and, in particular, tools to measure glucose levels, evolving from capillary measurements to continuous glucose monitoring, have revolutionised the way in which people living with diabetes use insulin. Novel insulin concepts, like once-weekly or oral insulin administration, will have to demonstrate safety on the side of hypoglycaemia before they will be able to move into the clinic.

An Analysis of QSAR Research Based on Machine Learning Concepts.

Quantitative Structure-Activity Relationship (QSAR) is a popular approach developed to correlate chemical molecules with their biological activities based on their chemical structures. Machine learning techniques have proved to be promising solutions to QSAR modeling. Due to the significant role of machine learning strategies in QSAR modeling, this area of research has attracted much attention from researchers. A considerable amount of literature has been published on machine learning based QSAR modeling methodologies whilst this domain still suffers from lack of a recent and comprehensive analysis of these algorithms. This study systematically reviews the application of machine learning algorithms in QSAR, aiming to provide an analytical framework. For this purpose, we present a framework called 'ML-QSAR'. This framework has been designed for future research to: a) facilitate the selection of proper strategies among existing algorithms according to the application area requirements, b) help to develop and ameliorate current methods and c) providing a platform to study existing methodologies comparatively. In ML-QSAR, first a structured categorization is depicted which studied the QSAR modeling research based on machine models. Then several criteria are introduced in order to assess the models. Finally, inspired by aforementioned criteria the qualitative analysis is carried out.

Insights into Machine Learning-based Approaches for Virtual Screening in Drug Discovery: Existing Strategies and Streamlining Through FP-CADD.

BACKGROUND: Machine learning is an active area of research in computer science by the availability of big data collection of all sorts prompting interest in the development of novel tools for data mining. Machine learning methods have wide applications in computer-aided drug discovery methods. Most incredible approaches to machine learning are used in drug designing, which further aid the process of biological modelling in drug discovery. Mainly, two main categories are present which are Ligand-Based Virtual Screening (LBVS) and Structure-Based Virtual Screening (SBVS), however, the machine learning approaches fall mostly in the category of LBVS. OBJECTIVES: This study exposits the major machine learning approaches being used in LBVS. Moreover, we have introduced a protocol named FP-CADD which depicts a 4-steps rule of thumb for drug discovery, the four protocols of computer-aided drug discovery (FP-CADD). Various important aspects along with SWOT analysis of FP-CADD are also discussed in this article. CONCLUSION: By this thorough study, we have observed that in LBVS algorithms, Support Vector Machines (SVM) and Random Forest (RF) are those which are widely used due to high accuracy and efficiency. These virtual screening approaches have the potential to revolutionize the drug designing field. Also, we believe that the process flow presented in this study, named FP-CADD, can streamline the whole process of computer-aided drug discovery. By adopting this rule, the studies related to drug discovery can be made homogeneous and this protocol can also be considered as an evaluation criterion in the peer-review process of research articles.

Novel approaches for the treatment of methicillin-resistant Staphylococcus aureus: Using nanoparticles to overcome multidrug resistance.

Methicillin-resistant Staphylococcus aureus (MRSA) causes serious infections in both community and hospital settings, with high mortality rates. Treatment of MRSA infections is challenging because of the rapidly evolving resistance mechanisms combined with the protective biofilms of S. aureus. Together, these characteristic resistance mechanisms continue to render conventional treatment modalities ineffective. The use of nanoformulations with unique modes of transport across bacterial membranes could be a useful strategy for disease-specific delivery. In this review, we summarize treatment approaches for MRSA, including novel techniques in nanoparticulate designing for better therapeutic outcomes; and facilitate an understanding that nanoparticulate delivery systems could be a robust approach in the successful treatment of MRSA.