In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm

Machine Learning-Driven Biomarker Discovery for Skeletal Complications in Type 1 Gaucher Disease Patients

Type 1 Gaucher disease (GD1) is a rare, autosomal recessive disorder caused by glucocerebrosidase deficiency. Skeletal manifestations represent one of the most debilitating and potentially irreversible complications of GD1. Although imaging studies are the gold standard, early diagnostic/prognostic tools, such as molecular biomarkers, are needed for the rapid management of skeletal complications. This study aimed to identify potential protein biomarkers capable of predicting the early diagnosis of bone skeletal complications in GD1 patients using artificial intelligence. An in silico study was performed using the novel Therapeutic Performance Mapping System methodology to construct mathematical models of GD1-associated complications at the protein level. Pathophysiological characterization was performed before modeling, and a data science strategy was applied to the predicted protein activity for each protein in the models to identify classifiers. Statistical criteria were used to prioritize the most promising candidates, and 18 candidates were identified. Among them, PDGFB, IL1R2, PTH and CCL3 (MIP-1α) were highlighted due to their ease of measurement in blood. This study proposes a validated novel tool to discover new protein biomarkers to support clinician decision-making in an area where medical needs have not yet been met. However, confirming the results using in vitro and/or in vivo studies is necessary.

Aflibercept Off-Target Effects in Diabetic Macular Edema: An In Silico Modeling Approach

Intravitreal aflibercept injection (IAI) is a treatment for diabetic macular edema (DME), but its mechanism of action (MoA) has not been completely elucidated. Here, we aimed to explore IAI's MoA and its multi-target nature in DME pathophysiology with an in silico (computer simulation) disease model. We used the Therapeutic Performance Mapping System (Anaxomics Biotech property) to generate mathematical models based on the available scientific knowledge at the time of the study, describing the relationship between the modulation of vascular endothelial growth factor receptors (VEGFRs) by IAI and DME pathophysiological processes. We also undertook an enrichment analysis to explore the processes modulated by IAI, visualized the effectors' predicted protein activity, and specifically evaluated the role of VEGFR1 pathway inhibition on DME treatment. The models simulated the potential pathophysiology of DME and the likely IAI's MoA by inhibiting VEGFR1 and VEGFR2 signaling. The action of IAI through both signaling pathways modulated the identified pathophysiological processes associated with DME, with the strongest effects in angiogenesis, blood-retinal barrier alteration and permeability, and inflammation. VEGFR1 inhibition was essential to modulate inflammatory protein effectors. Given the role of VEGFR1 signaling on the modulation of inflammatory-related pathways, IAI may offer therapeutic advantages for DME through sustained VEGFR1 pathway inhibition.

Assessing the antiviral potential of melatonin: A comprehensive systematic review

This review assesses the antiviral potential of melatonin through comprehensive analysis of studies across human subjects, animal models, cell cultures, and in-silico simulations. The search strategy targeted relevant research until 22 June 2023, resulting in 20 primary studies after screening and deduplication. The findings highlight strong evidence supporting antiviral properties of melatonin. In silico studies identify melatonin as a candidate against SARS-CoV-2, reducing cytokine storm-related respiratory responses. Cell culture experiments reveal its multifaceted effects on different viruses including respiratory syncytial virus, anti-dengue virus, transmissible gastroenteritis virus, and encephalomyocarditis virus. Animal studies show melatonin reduces mortality and viral replication in various infections such as Venezuelan equine encephalomyelitis and COVID-19. Clinical trials show how it could be evaluated, but with no conclusive evidence of efficacy and safety so far from large, double-blind placebo-controlled trials. These insights showcase the potential of melatonin as a versatile antiviral agent with immunomodulatory, antioxidant, anti-inflammatory and antiviral properties. In summary, our review highlights melatonin's promising antiviral properties across diverse settings. Melatonin's immunomodulatory and antiviral potential makes it a compelling candidate for further investigation, emphasising the need for rigorous clinical trials to establish its safety and efficacy against viral infections.

A quantitative systems pharmacology model for certolizumab pegol treatment in moderate-to-severe psoriasis

Background

Psoriasis is a chronic immune-mediated inflammatory systemic disease with skin manifestations characterized by erythematous, scaly, itchy and/or painful plaques resulting from hyperproliferation of keratinocytes. Certolizumab pegol [CZP], a PEGylated antigen binding fragment of a humanized monoclonal antibody against TNF-alpha, is approved for the treatment of moderate-to-severe plaque psoriasis. Patients with psoriasis present clinical and molecular variability, affecting response to treatment. Herein, we utilized an in silico approach to model the effects of CZP in a virtual population (vPop) with moderate-to-severe psoriasis. Our proof-of-concept study aims to assess the performance of our model in generating a vPop and defining CZP response variability based on patient profiles.

Methods

We built a quantitative systems pharmacology (QSP) model of a clinical trial-like vPop with moderate-to-severe psoriasis treated with two dosing schemes of CZP (200 mg and 400 mg, both every two weeks for 16 weeks, starting with a loading dose of CZP 400 mg at weeks 0, 2, and 4). We applied different modelling approaches: (i) an algorithm to generate vPop according to reference population values and comorbidity frequencies in real-world populations; (ii) physiologically based pharmacokinetic (PBPK) models of CZP dosing schemes in each virtual patient; and (iii) systems biology-based models of the mechanism of action (MoA) of the drug.

Results

The combination of our different modelling approaches yielded a vPop distribution and a PBPK model that aligned with existing literature. Our systems biology and QSP models reproduced known biological and clinical activity, presenting outcomes correlating with clinical efficacy measures. We identified distinct clusters of virtual patients based on their psoriasis-related protein predicted activity when treated with CZP, which could help unravel differences in drug efficacy in diverse subpopulations. Moreover, our models revealed clusters of MoA solutions irrespective of the dosing regimen employed.

Conclusion

Our study provided patient specific QSP models that reproduced clinical and molecular efficacy features, supporting the use of computational methods as modelling strategy to explore drug response variability. This might shed light on the differences in drug efficacy in diverse subpopulations, especially useful in complex diseases such as psoriasis, through the generation of mechanistically based hypotheses.

Polypharmacology guided drug repositioning approach for SARS-CoV2

Drug repurposing has emerged as an important strategy and it has a great potential in identifying therapeutic applications for COVID-19. An extensive virtual screening of 4193 FDA approved drugs has been carried out against 24 proteins of SARS-CoV2 (NSP1-10 and NSP12-16, envelope, membrane, nucleoprotein, spike, ORF3a, ORF6, ORF7a, ORF8, and ORF9b). The drugs were classified into top 10 and bottom 10 drugs based on the docking scores followed by the distribution of their therapeutic indications. As a result, the top 10 drugs were found to have therapeutic indications for cancer, pain, neurological disorders, and viral and bacterial diseases. As drug resistance is one of the major challenges in antiviral drug discovery, polypharmacology and network pharmacology approaches were employed in the study to identify drugs interacting with multiple targets and drugs such as dihydroergotamine, ergotamine, bisdequalinium chloride, midostaurin, temoporfin, tirilazad, and venetoclax were identified among the multi-targeting drugs. Further, a pathway analysis of the genes related to the multi-targeting drugs was carried which provides insight into the mechanism of drugs and identifying targetable genes and biological pathways involved in SARS-CoV2.

In silico evaluation of the role of lisdexamfetamine on attention-deficit/hyperactivity disorder common psychiatric comorbidities: mechanistic insights on binge eating disorder and depression

Attention-deficit/hyperactivity disorder (ADHD) is a psychiatric condition well recognized in the pediatric population that can persist into adulthood. The vast majority of patients with ADHD present psychiatric comorbidities that have been suggested to share, to some extent, the pathophysiological mechanism of ADHD. Lisdexamfetamine (LDX) is a stimulant prodrug approved for treating ADHD and, in the US, also for binge eating disorder (BED). Herein, we evaluated, through a systems biology-based in silico method, the efficacy of a virtual model of LDX (vLDX) as ADHD treatment to improve five common ADHD psychiatric comorbidities in adults and children, and we explored the molecular mechanisms behind LDX's predicted efficacy. After the molecular characterization of vLDX and the comorbidities (anxiety, BED, bipolar disorder, depression, and tics disorder), we created a protein-protein interaction human network to which we applied artificial neural networks (ANN) algorithms. We also generated virtual populations of adults and children-adolescents totaling 2,600 individuals and obtained the predicted protein activity from Therapeutic Performance Mapping System models. The latter showed that ADHD molecular description shared 53% of its protein effectors with at least one studied psychiatric comorbidity. According to the ANN analysis, proteins targeted by vLDX are predicted to have a high probability of being related to BED and depression. In BED, vLDX was modeled to act upon neurotransmission and neuroplasticity regulators, and, in depression, vLDX regulated the hypothalamic-pituitary-adrenal axis, neuroinflammation, oxidative stress, and glutamatergic excitotoxicity. In conclusion, our modeling results, despite their limitations and although requiring in vitro or in vivo validation, could supplement the design of preclinical and potentially clinical studies that investigate treatment for patients with ADHD with psychiatric comorbidities, especially from a molecular point of view.

Application of systems biology to identify pharmacological mechanisms of thrombotic microangiopathy evoked by combined activated prothrombin complex concentrate and emicizumab

Emicizumab is a bispecific monoclonal antibody that substitutes for the function of missing or deficient factor VIII (FVIII) in people with hemophilia A (PwHA). Long-term safety and efficacy of emicizumab have been demonstrated in several clinical trials. Nevertheless, in the first of these, three cases of thrombotic microangiopathy (TMA) occurred in PwHA treated with emicizumab receiving high doses of activated prothrombin complex concentrate (aPCC), a bypassing agent used for treating breakthrough bleeds when FVIII neutralizing antibodies (inhibitors) make FVIII replacement ineffective. The aim of the present work is to offer a method to elucidate the pathophysiological and pharmacological mechanisms involved in this treatment-induced TMA. Systems biology and machine learning-based Therapeutic Performance Mapping System is a validated in silico technology that allowed us to construct models of potential mechanisms behind induced TMA. Two drug combinations were modeled and assessed: emicizumab plus aPCC and emicizumab plus recombinant activated factor VII (another bypassing agent). Our models showed that both combinations were related to activation of the coagulation cascade. However, mechanisms involved mainly in platelet activation and possibly in complement activation were detected only for emicizumab plus aPCC, potentially explaining the occurrence of TMA only in this combination.

An in silico approach to identify early damage biomarker candidates in metachromatic leukodystrophy

Metachromatic leukodystrophy (MLD) is a rare, autosomal recessive lysosomal storage disease. Deficient activity of arylsulfatase A causes sulfatides to accumulate in cells of different tissues, including those in the central and peripheral nervous systems, leading to progressive demyelination and neurodegeneration. Although there is some association between specific arylsulfatase A alleles and disease severity, genotype-phenotype correlations are not fully understood. We aimed to identify biomarker candidates of early tissue damage in MLD using a modeling approach based on systems biology. A review of the literature was performed in an initial disease characterization step, allowing identification of pathophysiological processes involved in MLD and proteins relating to these processes. Three mathematical models were generated to simulate different stages of MLD at the molecular level: an early pro-inflammatory stage model (including only processes considered to be active in the early stages of disease), a pre-demyelination stage model (including additional processes that are active after some disease progression), and a demyelination stage model (in which all pathophysiological processes are active). The models evaluated 3457 proteins of interest, individually and by pairs through data mining techniques, applying five filters to prioritize biomarkers that could differentiate between the models. Sixteen potential biomarkers were identified, including effectors relating to mitochondrial dysfunction, remyelination, and neurodegeneration. The findings were corroborated in a gene expression data set from T lymphocytes of patients with MLD; all candidates formed combinations that were able to distinguish patients with MLD from controls, and all but one candidate distinguished late-infantile MLD from juvenile MLD as part of a combinatorial biomarker pair. In particular, pro-neuregulin-1 appeared as differential on all comparisons (patients with MLD vs controls and within clinical subtypes); casein kinase II subunit alpha was detected as a potential individual marker within clinical subtypes. These findings provide a panel of biomarker candidates suitable for experimental validation and highlight the utility of mathematical models to identify biomarker candidates of early tissue damage in MLD with a high degree of accuracy and sensitivity.

Development of indolealkylamine derivatives as potential multi-target agents for COVID-19 treatment

COVID-19 is a complex disease with short-term and long-term respiratory, inflammatory and neurological symptoms that are triggered by the infection with SARS-CoV-2. As many drugs targeting single targets showed only limited effectiveness against COVID-19, here, we aimed to explore a multi-target strategy. We synthesized a focused compound library based on C2-substituted indolealkylamines (tryptamines and 5-hydroxytryptamines) with activity for three potential COVID-19-related proteins, namely melatonin receptors, calmodulin and human angiotensin converting enzyme 2 (hACE2). Two molecules from the library, 5e and h, exhibit affinities in the high nanomolar range for melatonin receptors, inhibit the calmodulin-dependent calmodulin kinase II activity and the interaction of the SARS-CoV-2 Spike protein with hACE2 at micromolar concentrations. Both compounds inhibit SARS-CoV-2 entry into host cells and 5h decreases SARS-CoV-2 replication and M^Pro enzyme activity in addition. In conclusion, we provide a proof-of-concept for the successful design of multi-target compounds based on the tryptamine scaffold. Optimization of these preliminary hit compounds could potentially provide drug candidates to treat COVID-19 and other coronavirus diseases.

Artificial intelligence assessment of the potential of tocilizumab along with corticosteroids therapy for the management of COVID-19 evoked acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS), associated with high mortality rate, affects up to 67% of hospitalized COVID-19 patients. Early evidence indicated that the pathogenesis of COVID-19 evoked ARDS is, at least partially, mediated by hyperinflammatory cytokine storm in which interleukin 6 (IL-6) plays an essential role. The corticosteroid dexamethasone is an effective treatment for severe COVID-19 related ARDS. However, trials of other immunomodulatory therapies, including anti-IL6 agents such as tocilizumab and sarilumab, have shown limited evidence of benefit as monotherapy. But recently published large trials have reported added benefit of tocilizumab in combination with dexamethasone in severe COVID-19 related ARDS. In silico tools can be useful to shed light on the mechanisms evoked by SARS-CoV-2 infection and of the potential therapeutic approaches. Therapeutic performance mapping system (TPMS), based on systems biology and artificial intelligence, integrate available biological, pharmacological and medical knowledge to create mathematical models of the disease. This technology was used to identify the pharmacological mechanism of dexamethasone, with or without tocilizumab, in the management of COVID-19 evoked ARDS. The results showed that while dexamethasone would be addressing a wider range of pathological processes with low intensity, tocilizumab might provide a more direct and intense effect upon the cytokine storm. Based on this in silico study, we conclude that the use of tocilizumab alongside dexamethasone is predicted to induce a synergistic effect in dampening inflammation and subsequent pathological processes, supporting the beneficial effect of the combined therapy in critically ill patients. Future research will allow identifying the ideal subpopulation of patients that would benefit better from this combined treatment.

Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence

Machine Learning (ML) is an algorithm based on big data, which learns patterns from the previously observed data through classifying, predicting, and optimizing to accomplish specific tasks. In recent years, there has been rapid development in the field of ML in medicine, including lung imaging analysis, intensive medical monitoring, mechanical ventilation, and there is need for intubation etiology prediction evaluation, pulmonary function evaluation and prediction, obstructive sleep apnea, such as biological information monitoring and so on. ML can have good performance and is a great potential tool, especially in the imaging diagnosis of interstitial lung disease. Idiopathic pulmonary fibrosis (IPF) is a major problem in the treatment of respiratory diseases, due to the abnormal proliferation of fibroblasts, leading to lung tissue destruction. The diagnosis mainly depends on the early detection of imaging and early treatment, which can effectively prolong the life of patients. If the computer can be used to assist the examination results related to the effects of fibrosis, a timely diagnosis of such diseases will be of great value to both doctors and patients. We also previously proposed a machine learning algorithm model that can play a good clinical guiding role in early imaging prediction of idiopathic pulmonary fibrosis. At present, AI and machine learning have great potential and ability to transform many aspects of respiratory medicine and are the focus and hotspot of research. AI needs to become an invisible, seamless, and impartial auxiliary tool to help patients and doctors make better decisions in an efficient, effective, and acceptable way. The purpose of this paper is to review the current application of machine learning in various aspects of respiratory diseases, with the hope to provide some help and guidance for clinicians when applying algorithm models.

Unveiling chronic spontaneous urticaria pathophysiology through systems biology

BACKGROUND

Chronic spontaneous urticaria (CSU) is a rare, heterogeneous, severely debilitating, and often poorly controlled skin disease resulting in an itchy eruption that can be persistent. Antihistamines and omalizumab, an anti-IgE mAb, are the only licensed therapies. Although CSU pathogenesis is not yet fully understood, mast cell activation through the IgE:high-affinity IgE receptor (FcεRI) axis appears central to the disease process.

OBJECTIVE

We sought to model CSU pathophysiology and identify in silico the mechanism of action of different CSU therapeutic strategies currently in use or under development.

METHODS

Therapeutic performance mapping system technology, based on systems biology and machine learning, was used to create a CSU interactome validated with gene expression data from patients with CSU and a CSU model that was used to evaluate CSU pathophysiology and the mechanism of action of different therapeutic strategies.

RESULTS

Our models reflect the known role of mast cell activation as a central process of CSU pathophysiology, as well as recognized roles for different therapeutic strategies in this and other innate and adaptive immune processes. They also allow determining similarities and differences between them; anti-IgE and Bruton tyrosine kinase inhibitors play a more direct role in mast cell biology through abrogation of FcεRI signaling activity, whereas anti-interleukins and anti-Siglec-8 have a role in adaptive immunity modulation.

CONCLUSION

In silico CSU models reproduced known CSU and therapeutic strategies features. Our results could help advance understanding of therapeutic mechanisms of action and further advance treatment research by patient profile.

Possible Application of Melatonin in Long COVID

Clinical sequelae and symptoms for a considerable number of COVID-19 patients can linger for months beyond the acute stage of SARS-CoV-2 infection, "long COVID". Among the long-term consequences of SARS-CoV-2 infection, cognitive issues (especially memory loss or "brain fog"), chronic fatigue, myalgia, and muscular weakness resembling myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) are of importance. Melatonin may be particularly effective at reducing the signs and symptoms of SARS-CoV-2 infection due to its functions as an antioxidant, anti-inflammatory, and immuno-modulatory agent. Melatonin is also a chronobiotic medication effective in treating delirium and restoring the circadian imbalance seen in COVID patients in the intensive care unit. Additionally, as a cytoprotector, melatonin aids in the prevention of several COVID-19 comorbidities, including diabetes, metabolic syndrome, and ischemic and non-ischemic cardiovascular diseases. This narrative review discusses the application of melatonin as a neuroprotective agent to control cognitive deterioration ("brain fog") and pain in the ME/CFS syndrome-like documented in long COVID. Further studies on the therapeutic use of melatonin in the neurological sequelae of SARS-CoV-2 infection are warranted.

Potential Therapeutic Approach of Melatonin against Omicron and Some Other Variants of SARS-CoV-2

The Omicron variant (B.529) of COVID-19 caused disease outbreaks worldwide because of its contagious and diverse mutations. To reduce these outbreaks, therapeutic drugs and adjuvant vaccines have been applied for the treatment of the disease. However, these drugs have not shown high efficacy in reducing COVID-19 severity, and even antiviral drugs have not shown to be effective. Researchers thus continue to search for an effective adjuvant therapy with a combination of drugs or vaccines to treat COVID-19 disease. We were motivated to consider melatonin as a defensive agent against SARS-CoV-2 because of its various unique properties. Over 200 scientific publications have shown the significant effects of melatonin in treating diseases, with strong antioxidant, anti-inflammatory, and immunomodulatory effects. Melatonin has a high safety profile, but it needs further clinical trials and experiments for use as a therapeutic agent against the Omicron variant of COVID-19. It might immediately be able to prevent the development of severe symptoms caused by the coronavirus and can reduce the severity of the infection by improving immunity.

Systems biology and artificial intelligence analysis highlights the pleiotropic effect of IVIg therapy in autoimmune diseases with a predominant role on B cells and complement system

Intravenous immunoglobulin (IVIg) is used as treatment for several autoimmune and inflammatory conditions, but its specific mechanisms are not fully understood. Herein, we aimed to evaluate, using systems biology and artificial intelligence techniques, the differences in the pathophysiological pathways of autoimmune and inflammatory conditions that show diverse responses to IVIg treatment. We also intended to determine the targets of IVIg involved in the best treatment response of the evaluated diseases. Our selection and classification of diseases was based on a previously published systematic review, and we performed the disease characterization through manual curation of the literature. Furthermore, we undertook the mechanistic evaluation with artificial neural networks and pathway enrichment analyses. A set of 26 diseases was selected, classified, and compared. Our results indicated that diseases clearly benefiting from IVIg treatment were mainly characterized by deregulated processes in B cells and the complement system. Indeed, our results show that proteins related to B-cell and complement system pathways, which are targeted by IVIg, are involved in the clinical response. In addition, targets related to other immune processes may also play an important role in the IVIg response, supporting its wide range of actions through several mechanisms. Although B-cell responses and complement system have a key role in diseases benefiting from IVIg, protein targets involved in such processes are not necessarily the same in those diseases. Therefore, IVIg appeared to have a pleiotropic effect that may involve the collaborative participation of several proteins. This broad spectrum of targets and 'non-specificity' of IVIg could be key to its efficacy in very different diseases.

Pirfenidone and post-Covid-19 pulmonary fibrosis: invoked again for realistic goals

Pirfenidone (PFN) is an anti-fibrotic drug with significant anti-inflammatory property used for treatment of fibrotic conditions such as idiopathic pulmonary fibrosis (IPF). In the coronavirus disease 2019 (Covid-19) era, severe acute respiratory syndrome 2 (SARS-CoV-2) could initially lead to acute lung injury (ALI) and in severe cases may cause acute respiratory distress syndrome (ARDS) which is usually resolved with normal lung function. However, some cases of ALI and ARDS are progressed to the more severe critical stage of pulmonary fibrosis commonly named post-Covid-19 pulmonary fibrosis which needs an urgent address and proper management. Therefore, the objective of the present study was to highlight the potential role of PFN in the management of post-Covid-19 pulmonary fibrosis. The precise mechanism of post-Covid-19 pulmonary fibrosis is related to the activation of transforming growth factor beta (TGF-β1), which activates the release of extracellular proteins, fibroblast proliferation, fibroblast migration and myofibroblast conversion. PFN inhibits accumulation and recruitment of inflammatory cells, fibroblast proliferation, deposition of extracellular matrix in response to TGFβ1 and other pro-inflammatory cytokines. In addition, PFN suppresses furin (TGFβ1 convertase activator) a protein effector involved in the entry of SARS-CoV-2 and activation of TGFβ1, and thus PFN reduces the pathogenesis of SARS-CoV-2. Besides, PFN modulates signaling pathways such as Wingless/Int (Wnt/β-catenin), Yes-Associated Protein (YAP)/Transcription Co-Activator PDZ Binding Motif (TAZ) and Hippo Signaling Pathways that are involved in the pathogenesis of post-Covid-19 pulmonary fibrosis. In conclusion, the anti-inflammatory and anti-fibrotic properties of PFN may attenuate post-Covid-19 pulmonary fibrosis.

Prediction of Adverse Drug Reaction Linked to Protein Targets Using Network-Based Information and Machine Learning

Drug discovery attrition rates, particularly at advanced clinical trial stages, are high because of unexpected adverse drug reactions (ADR) elicited by novel drug candidates. Predicting undesirable ADRs produced by the modulation of certain protein targets would contribute to developing safer drugs, thereby reducing economic losses associated with high attrition rates. As opposed to the more traditional drug-centric approach, we propose a target-centric approach to predict associations between protein targets and ADRs. The implementation of the predictor is based on a machine learning classifier that integrates a set of eight independent network-based features. These include a network diffusion-based score, identification of protein modules based on network clustering algorithms, functional similarity among proteins, network distance to proteins that are part of safety panels used in preclinical drug development, set of network descriptors in the form of degree and betweenness centrality measurements, and conservation. This diverse set of descriptors were used to generate predictors based on different machine learning classifiers ranging from specific models for individual ADR to higher levels of abstraction as per MEDDRA hierarchy such as system organ class. The results obtained from the different machine-learning classifiers, namely, support vector machine, random forest, and neural network were further analyzed as a meta-predictor exploiting three different voting systems, namely, jury vote, consensus vote, and red flag, obtaining different models for each of the ADRs in analysis. The level of accuracy of the predictors justifies the identification of problematic protein targets both at the level of individual ADR as well as a set of related ADRs grouped in common system organ classes. As an example, the prediction of ventricular tachycardia achieved an accuracy and precision of 0.83 and 0.90, respectively, and a Matthew correlation coefficient of 0.70. We believe that this approach is a good complement to the existing methodologies devised to foresee potential liabilities in preclinical drug discovery. The method is available through the DocTOR utility at GitHub (https://github.com/cristian931/DocTOR).

A Review of the Potential Effects of Melatonin in Compromised Mitochondrial Redox Activities in Elderly Patients With COVID-19

Melatonin, an endogenous indoleamine, is an antioxidant and anti-inflammatory molecule widely distributed in the body. It efficiently regulates pro-inflammatory and anti-inflammatory cytokines under various pathophysiological conditions. The melatonin rhythm, which is strongly associated with oxidative lesions and mitochondrial dysfunction, is also observed during the biological process of aging. Melatonin levels decline considerably with age and are related to numerous age-related illnesses. The signs of aging, including immune aging, increased basal inflammation, mitochondrial dysfunction, significant telomeric abrasion, and disrupted autophagy, contribute to the increased severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. These characteristics can worsen the pathophysiological response of the elderly to SARS-CoV-2 and pose an additional risk of accelerating biological aging even after recovery. This review explains that the death rate of coronavirus disease (COVID-19) increases with chronic diseases and age, and the decline in melatonin levels, which is closely related to the mitochondrial dysfunction in the patient, affects the virus-related death rate. Further, melatonin can enhance mitochondrial function and limit virus-related diseases. Hence, melatonin supplementation in older people may be beneficial for the treatment of COVID-19.

Melatonin drugs inhibit SARS-CoV-2 entry into the brain and virus-induced damage of cerebral small vessels

COVID-19 is a complex disease with short- and long-term respiratory, inflammatory and neurological symptoms that are triggered by the infection with SARS-CoV-2. Invasion of the brain by SARS-CoV-2 has been observed in humans and is postulated to be involved in post-COVID state. Brain infection is particularly pronounced in the K18-hACE2 mouse model of COVID-19. Prevention of brain infection in the acute phase of the disease might thus be of therapeutic relevance to prevent long-lasting symptoms of COVID-19. We previously showed that melatonin or two prescribed structural analogs, agomelatine and ramelteon delay the onset of severe clinical symptoms and improve survival of SARS-CoV-2-infected K18-hACE2 mice. Here, we show that treatment of K18-hACE2 mice with melatonin and two melatonin-derived marketed drugs, agomelatine and ramelteon, prevents SARS-CoV-2 entry in the brain, thereby reducing virus-induced damage of small cerebral vessels, immune cell infiltration and brain inflammation. Molecular modeling analyses complemented by experimental studies in cells showed that SARS-CoV-2 entry in endothelial cells is prevented by melatonin binding to an allosteric-binding site on human angiotensin-converting enzyme 2 (ACE2), thus interfering with ACE2 function as an entry receptor for SARS-CoV-2. Our findings open new perspectives for the repurposing of melatonergic drugs and its clinically used analogs in the prevention of brain infection by SARS-CoV-2 and COVID-19-related long-term neurological symptoms.

Identification of the Potential Molecular Mechanisms Linking RUNX1 Activity with Nonalcoholic Fatty Liver Disease, by Means of Systems Biology

Nonalcoholic fatty liver disease (NAFLD) is the most prevalent chronic hepatic disease; nevertheless, no definitive diagnostic method exists yet, apart from invasive liver biopsy, and nor is there a specific approved treatment. Runt-related transcription factor 1 (RUNX1) plays a major role in angiogenesis and inflammation; however, its link with NAFLD is unclear as controversial results have been reported. Thus, the objective of this work was to determine the proteins involved in the molecular mechanisms between RUNX1 and NAFLD, by means of systems biology. First, a mathematical model that simulates NAFLD pathophysiology was generated by analyzing Anaxomics databases and reviewing available scientific literature. Artificial neural networks established NAFLD pathophysiological processes functionally related to RUNX1: hepatic insulin resistance, lipotoxicity, and hepatic injury-liver fibrosis. Our study indicated that RUNX1 might have a high relationship with hepatic injury-liver fibrosis, and a medium relationship with lipotoxicity and insulin resistance motives. Additionally, we found five RUNX1-regulated proteins with a direct involvement in NAFLD motives, which were NFκB1, NFκB2, TNF, ADIPOQ, and IL-6. In conclusion, we suggested a relationship between RUNX1 and NAFLD since RUNX1 seems to regulate NAFLD molecular pathways, posing it as a potential therapeutic target of NAFLD, although more studies in this field are needed.

Efficacy of melatonin in the treatment of patients with COVID-19: A systematic review and meta-analysis of randomized controlled trials

This study investigated the effect of melatonin on clinical outcomes in patients with coronavirus disease 2019 (COVID-19). We searched PubMed, the Web of Science, the Cochrane Library, Ovid MEDLINE, and Clinicaltrials.gov for randomized controlled trials (RCTs) published before September 11, 2021. Only RCTs that compared the clinical efficacy of melatonin with a placebo in the treatment of patients with COVID-19 were included. The primary outcome measure was the clinical recovery rate. We included three RCTs in this meta-analysis. Melatonin 3 mg three times daily was administered in one RCT, and 3 or 6 mg daily before bedtime in the other two trials. Treatment duration was 14 days in two RCTs and 7 days in one trial. The clinical recovery rates were 94.2% (81/86) and 82.4% (70/85) in the melatonin and control groups, respectively. Overall, patients receiving melatonin had a higher clinical recovery rate than did the controls (odds ratio [OR]: 3.67; 95% CI: 1.21-11.12; I²  = 0%, p = 0.02). The risk of intensive care unit admission was numerically lower in the melatonin group than in the control group (8.3% [6/72] vs. 17.6% [12/68], OR: 0.45; 95% CI: 0.16-1.25; I²  = 0%, p = 0.13), and the risk of mortality was numerically lower in the melatonin group than in the control group (1.4% [1/72] vs. 4.4% [3/68], OR: 0.32; 95% CI: 0.03-3.18; I²  = 0%, p = 0.33). In conclusion, melatonin may help improve the clinical outcomes of patients with COVID-19.

The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19

With Covid-19 impacting communities in different ways, research has increasingly turned to big data analytics (BDA) and artificial intelligence (AI) tools to track and monitor the virus's spread and its effect on humanity and the global economy. The purpose of this study is to conduct an in-depth literature review to identify how BDA and AI were involved in the management of Covid-19 (while considering diversity, equity, and inclusion (DEI)). The rigorous search resulted in a portfolio of 607 articles, retrieved from the Web of Science database, where content analysis has been conducted. This study identifies the BDA and AI applications developed to deal with the initial Covid-19 outbreak and the containment of the pandemic, along with their benefits for the social good. Moreover, this study reveals the DEI challenges related to these applications, ways to mitigate the concerns, and how to develop viable techniques to deal with similar crises in the future. The article pool recognized the high presence of machine learning (ML) and the role of mobile technology, social media and telemedicine in the use of BDA and AI during Covid-19. This study offers a collective insight into many of the key issues and underlying complexities affecting public health and society from Covid-19, and the solutions offered from information systems and technological perspectives.

Identification of Potential Muscle Biomarkers in McArdle Disease: Insights from Muscle Proteome Analysis

Glycogen storage disease type V (GSDV, McArdle disease) is a rare genetic myopathy caused by deficiency of the muscle isoform of glycogen phosphorylase (PYGM). This results in a block in the use of muscle glycogen as an energetic substrate, with subsequent exercise intolerance. The pathobiology of GSDV is still not fully understood, especially with regard to some features such as persistent muscle damage (i.e., even without prior exercise). We aimed at identifying potential muscle protein biomarkers of GSDV by analyzing the muscle proteome and the molecular networks associated with muscle dysfunction in these patients. Muscle biopsies from eight patients and eight healthy controls showing none of the features of McArdle disease, such as frequent contractures and persistent muscle damage, were studied by quantitative protein expression using isobaric tags for relative and absolute quantitation (iTRAQ) followed by artificial neuronal networks (ANNs) and topology analysis. Protein candidate validation was performed by Western blot. Several proteins predominantly involved in the process of muscle contraction and/or calcium homeostasis, such as myosin, sarcoplasmic/endoplasmic reticulum calcium ATPase 1, tropomyosin alpha-1 chain, troponin isoforms, and alpha-actinin-3, showed significantly lower expression levels in the muscle of GSDV patients. These proteins could be potential biomarkers of the persistent muscle damage in the absence of prior exertion reported in GSDV patients. Further studies are needed to elucidate the molecular mechanisms by which PYGM controls the expression of these proteins.

Ceruletide and Alpha-1 Antitrypsin as a Novel Combination Therapy for Ischemic Stroke

Ischemic stroke is a primary cause of morbidity and mortality worldwide. Beyond the approved thrombolytic therapies, there is no effective treatment to mitigate its progression. Drug repositioning combinational therapies are becoming promising approaches to identify new uses of existing drugs to synergically target multiple disease-response mechanisms underlying complex pathologies. Here, we used a systems biology-based approach based on artificial intelligence and pattern recognition tools to generate in silico mathematical models mimicking the ischemic stroke pathology. Combinational treatments were acquired by screening these models with more than 5 million two-by-two combinations of drugs. A drug combination (CA) formed by ceruletide and alpha-1 antitrypsin showing a predicted value of neuroprotection of 92% was evaluated for their synergic neuroprotective effects in a mouse pre-clinical stroke model. The administration of both drugs in combination was safe and effective in reducing by 39.42% the infarct volume 24 h after cerebral ischemia. This neuroprotection was not observed when drugs were given individually. Importantly, potential incompatibilities of the drug combination with tPA thrombolysis were discarded in vitro and in vivo by using a mouse thromboembolic stroke model with t-PA-induced reperfusion, revealing an improvement in the forepaw strength 72 h after stroke in CA-treated mice. Finally, we identified the predicted mechanisms of action of ceruletide and alpha-1 antitrypsin and we demonstrated that CA modulates EGFR and ANGPT-1 levels in circulation within the acute phase after stroke. In conclusion, we have identified a promising combinational treatment with neuroprotective effects for the treatment of ischemic stroke.

Melatonin: highlighting its use as a potential treatment for SARS-CoV-2 infection

Numerous pharmaceutical drugs have been repurposed for use as treatments for COVID-19 disease. These drugs have not consistently demonstrated high efficacy in preventing or treating this serious condition and all have side effects to differing degrees. We encourage the continued consideration of the use of the antioxidant and anti-inflammatory agent, melatonin, as a countermeasure to a SARS-CoV-2 infection. More than 140 scientific publications have identified melatonin as a likely useful agent to treat this disease. Moreover, the publications cited provide the rationale for the use of melatonin as a prophylactic agent against this condition. Melatonin has pan-antiviral effects and it diminishes the severity of viral infections and reduces the death of animals infected with numerous different viruses, including three different coronaviruses. Network analyses, which compared drugs used to treat SARS-CoV-2 in humans, also predicted that melatonin would be the most effective agent for preventing/treating COVID-19. Finally, when seriously infected COVID-19 patients were treated with melatonin, either alone or in combination with other medications, these treatments reduced the severity of infection, lowered the death rate, and shortened the duration of hospitalization. Melatonin’s ability to arrest SARS-CoV-2 infections may reduce health care exhaustion by limiting the need for hospitalization. Importantly, melatonin has a high safety profile over a wide range of doses and lacks significant toxicity. Some molecular processes by which melatonin resists a SARS-CoV-2 infection are summarized. The authors believe that all available, potentially beneficial drugs, including melatonin, that lack toxicity should be used in pandemics such as that caused by SARS-CoV-2.

Exploring the synergistic effects of cabozantinib and a programmed cell death protein 1 inhibitor in metastatic renal cell carcinoma with machine learning

Clinical evidence supports the combination of cabozantinib with an immune checkpoint inhibitor for the treatment of metastatic clear cell renal cell carcinoma (mccRCC) and suggests a synergistic antitumour activity of this combination. Nevertheless, the biological basis of this synergy is not fully characterized. We studied the mechanisms underpinning the potential synergism of cabozantinib combined with a PD1 inhibitor in mccRCC and delved into cabozantinib monotherapy properties supporting its role to partner these combinations. To model physiological drug action, we used a machine learning-based technology known as Therapeutic Performance Mapping Systems, applying two approaches: Artificial Neural Networks and Sampling Methods. We found that the combined therapy was predicted to exert a wide therapeutic action in the tumour and the microenvironment. Cabozantinib may enhance the effects of PD1 inhibitors on immunosurveillance by modulating the innate and adaptive immune system, through the inhibition of VEGF-VEGFR and Gas6-AXL/TYRO3/MER (TAM) axes, while the PD1 inhibitors may boost the antiangiogenic and pro-apoptotic effects of cabozantinib by modulating angiogenesis and T-cell cytotoxicity. Cabozantinib alone was predicted to restore cellular adhesion and hamper tumour proliferation and invasion. These data provide a biological rationale and further support for cabozantinib plus PD1 inhibitor combination and may guide future nonclinical and clinical research.

Potential and Possible Therapeutic Effects of Melatonin on SARS-CoV-2 Infection

The absence of effective drugs for COVID-19 prevention and treatment requires the search for new candidates among approved medicines. Fundamental studies and clinical observations allow us to approach an understanding of the mechanisms of damage and protection from exposure to SARS-CoV-2, to identify possible points of application for pharmacological interventions. In this review we presented studies on the anti-inflammatory, antioxidant, and immunotropic properties of melatonin. We have attempted to present scientifically proven mechanisms of action for the potential therapeutic use of melatonin during SARS-CoV-2 infection. A wide range of pharmacological properties allows its inclusion as an effective addition to the methods of prevention and treatment of COVID-19.

Protective Effect of Melatonin Administration against SARS-CoV-2 Infection: A Systematic Review

Introduction: according to the World Health Organization (WHO), COVID-19 is an infectious disease caused by the SARS-CoV-2 virus, responsible for an increasing number of cases and deaths. From a preventive and therapeutic point of view, there are two concerns that affect institutions and healthcare professionals: global immunization (which is still far from being achieved) and the availability of drugs capable of preventing its consequences in the infected patient. In this sense, the role that melatonin can play is has been assessed in the recent literature. Justification and Objectives: the serious health, social and economic consequences of COVID-19 have forced an urgent search for preventive methods, such as vaccines, among others, and therapeutic methods that could be alternatives to the drugs currently used. In this sense, it must be accepted that one of the most recommended has been the administration of melatonin. The present study proposes to carry out a systematic review of its possible role in the treatment and/or prevention of COVID-19. Material and methods: a systematic review of the literature related to the prevention of COVID-19 through the administration of melatonin was carried out, following the sequence proposed by the Prisma Declaration regarding the identification and selection of documents, using the specialized health databases Trip Medical Database, Cochrane Library, PubMed, Medline Plus, BVS, Cuiden and generic databases such as Dialnet, Web of Science and Google Scholar for their retrieval. Appropriate inclusion and exclusion criteria are described for the articles assessed. The main limitation of the study has been the scarcity of works and the lack of defining a specific protocol in terms of dosage and administration schedule. Results: once the selection process was completed, and after an in-depth critical analysis, 197 papers were selected, and 40 of them were finally used. The most relevant results were: (1) melatonin prevents SARS-CoV-2 infection, (2) although much remains to be clarified, at high doses, it seems to have a coadjuvant therapeutic effect in the treatment of SARS-CoV-2 infection and (3) melatonin is effective against SARS-CoV-2 infection. Discussion: until group immunization is achieved in the population, it seems clear that we must continue to treat patients with SARS-CoV-2 infection, and, in the absence of a specific and effective antiviral therapy, it is advisable to continue researching and providing drugs that demonstrate validity based on the scientific evidence. In this regard, we believe that the available studies recommend the administration of melatonin for its anti-inflammatory, antioxidant, immunomodulatory, sleep-inducing, CD147, Mpro, p65 and MMP9 protein suppressing, nephrotoxicity-reducing and highly effective and safe effects. Conclusions: (1) melatonin has anti-inflammatory, antioxidant, immunomodulatory, and Mpro and MMP9 protein-inhibitory activity. (2) It has been shown to have a wide margin of safety. (3) The contributions reviewed make it an effective therapeutic alternative in the treatment of SARS-CoV-2 infection. (4) Further clinical trials are recommended to clearly define the administration protocol.

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.

A Systematic Review on the Contribution of Artificial Intelligence in the Development of Medicines for COVID-2019

Background: COVID-2019 pandemic lead to a raised interest on the development of new treatments through Artificial Intelligence (AI). Aim: to carry out a systematic review on the development of repurposed drugs against COVID-2019 through the application of AI. Methods: The Systematic Reviews and Meta-Analyses (PRISMA) checklist was applied. Keywords: [“Artificial intelligence” and (COVID or SARS) and (medicine or drug)]. Databases: PubMed^®, DOAJ and SciELO. Cochrane Library was additionally screened to identify previous published reviews on the same topic. Results: From the 277 identified records [PubMed^® (n = 157); DOAJ (n = 119) and SciELO (n = 1)], 27 studies were included. Among other, the selected studies on new treatments against COVID-2019 were classified, as follows: studies with in-vitro and/or clinical data; association of known drugs; and other studies related to repurposing of drugs. Conclusion: Diverse potentially repurposed drugs against COVID-2019 were identified. The repurposed drugs were mainly from antivirals, antibiotics, anticancer, anti-inflammatory, and Angiotensin-converting enzyme 2 (ACE2) groups, although diverse other pharmacologic groups were covered. AI was a suitable tool to quickly analyze large amounts of data or to estimate drug repurposing against COVID-2019.

Candidate Psychotropics against SARS - CoV - 2: A Narrative Review

Since few therapeutic options are clinically accessible for coronavirus disease 2019 (COVID-19), effective, safe, and globally available pharmaceuticals need to be urgently developed to prevent severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) and alleviate the severity of COVID-19. In this regard, the present paper is intended to provide an extensive review of the clinical and preclinical evidence on the psychotropics' anti-SARS-CoV-2 effects, giving an insight into their potential applications for patients with a proven or high likelihood of COVID-19 pneumonia. The results showed that psychotropic drugs such as melatonin, lithium carbonate, valproate, olanzapine, quetiapine, clozapine, fluoxetine, escitalopram, fluvoxamine, and cannabidiol could help lower the mortality due to SARS-CoV-2 infection. According to these medications' direct immunomodulatory actions against the destructive cytokine storm, as well as other direct/indirect mechanisms (e. g., the endolysosomal pathway modulation, interactions with specific receptors, and membrane fusion), it was perceived that such drugs could effectively weaken the worsened immune response and avoid adult respiratory distress syndrome and acute lung injury. According to the author's analysis of the currently available evidence, there is significant support for psychotropics as complementary interventions during SARS-CoV-2 infection. However, further studies need to be carried out to assess the effects of the above psychotropic drugs in vitro and clinical settings.

Melatonin as a Potential Adjuvant Treatment for COVID-19 beyond Sleep Disorders

Melatonin is registered to treat circadian rhythm sleep–wake disorders and insomnia in patients aged 55 years and over. The essential role of the circadian sleep rhythm in the deterioration of sleep quality during COVID-19 confinement and the lack of an adverse effect of melatonin on respiratory drive indicate that melatonin has the potential to be a recommended treatment for sleep disturbances related to COVID-19. This review article describes the effects of melatonin additional to its sleep-related effects, which make this drug an attractive therapeutic option for treating patients with COVID-19. The preclinical data suggest that melatonin may inhibit COVID-19 progression. It may lower the risk of the entrance of the SARS-CoV-2 virus into cells, reduce uncontrolled hyper-inflammation and the activation of immune cells, limit the damage of tissues and multiorgan failure due to the action of free radicals, and reduce ventilator-induced lung injury and the risk of disability resulting from fibrotic changes within the lungs. Melatonin may also increase the efficacy of COVID-19 vaccination. The high safety profile of melatonin and its potential anti-SARS-CoV-2 effects make this molecule a preferable drug for treating sleep disturbances in COVID-19 patients. However, randomized clinical trials are needed to verify the clinical usefulness of melatonin in the treatment of COVID-19.

A systems-level study reveals host-targeted repurposable drugs against SARS-CoV-2 infection

Understanding the mechanism of SARS-CoV-2 infection and identifying potential therapeutics are global imperatives. Using a quantitative systems pharmacology approach, we identified a set of repurposable and investigational drugs as potential therapeutics against COVID-19. These were deduced from the gene expression signature of SARS-CoV-2-infected A549 cells screened against Connectivity Map and prioritized by network proximity analysis with respect to disease modules in the viral-host interactome. We also identified immuno-modulating compounds aiming at suppressing hyperinflammatory responses in severe COVID-19 patients, based on the transcriptome of ACE2-overexpressing A549 cells. Experiments with Vero-E6 cells infected by SARS-CoV-2, as well as independent syncytia formation assays for probing ACE2/SARS-CoV-2 spike protein-mediated cell fusion using HEK293T and Calu-3 cells, showed that several predicted compounds had inhibitory activities. Among them, salmeterol, rottlerin, and mTOR inhibitors exhibited antiviral activities in Vero-E6 cells; imipramine, linsitinib, hexylresorcinol, ezetimibe, and brompheniramine impaired viral entry. These novel findings provide new paths for broadening the repertoire of compounds pursued as therapeutics against COVID-19.

Melatonin multifaceted pharmacological actions on melatonin receptors converging to abrogate COVID-19

Data indicate that controlling inflammatory responses to COVID-19 may be as important as antiviral therapies or could be an important adjunctive approach. Melatonin possesses anti-inflammation, antioxidation, and immune-enhancing features directly and/or indirectly through its own receptor signaling and is therefore well suited to reduce the severity of COVID-19. Studies have proposed that melatonin regulates COVID-19-associated proteins directly through regulation of molecules such as calmodulin (CALM) 1 and CALM 2, calreticulin (CalR), or myeloperoxidase (MPO) and/or indirectly through actions on GPCR (eg, MTNR1A, MTNR1B) and nuclear (eg, RORα, RORβ) melatonin receptor signaling. However, the exact mechanism(s) and doses by which melatonin reduces the severity of COVID-19 is still open for debate, warranting the need for further testing of melatonin in placebo-controlled randomized clinical trials for COVID-19.

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2

It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing, and development, and motivate researchers in harnessing AI potentials in the fight against COVID-19.

The Coronavirus Disease 2019 (COVID-19): Key Emphasis on Melatonin Safety and Therapeutic Efficacy

Viral infections constitute a tectonic convulsion in the normophysiology of the hosts. The current coronavirus disease 2019 (COVID-19) pandemic is not an exception, and therefore the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, like any other invading microbe, enacts a generalized immune response once the virus contacts the body. Melatonin is a systemic dealer that does not overlook any homeostasis disturbance, which consequently brings into play its cooperative triad, antioxidant, anti-inflammatory, and immune-stimulant backbone, to stop the infective cycle of SARS-CoV-2 or any other endogenous or exogenous threat. In COVID-19, the corporal propagation of SARS-CoV-2 involves an exacerbated oxidative activity and therefore the overproduction of great amounts of reactive oxygen and nitrogen species (RONS). The endorsement of melatonin as a possible protective agent against the current pandemic is indirectly supported by its widely demonstrated beneficial role in preclinical and clinical studies of other respiratory diseases. In addition, focusing the therapeutic action on strengthening the host protection responses in critical phases of the infective cycle makes it likely that multi-tasking melatonin will provide multi-protection, maintaining its efficacy against the virus variants that are already emerging and will emerge as long as SARS-CoV-2 continues to circulate among us.

Update on Functional Inhibitors of Acid Sphingomyelinase (FIASMAs) in SARS-CoV-2 Infection

The SARS-CoV-2 outbreak is characterized by the need of the search for curative drugs for treatment. In this paper, we present an update of knowledge about the interest of the functional inhibitors of acid sphingomyelinase (FIASMAs) in SARS-CoV-2 infection. Forty-nine FIASMAs have been suggested in the treatment of SARS-CoV-2 infection using in silico, in vitro or in vivo studies. Further studies using large-sized, randomized and double-blinded controlled clinical trials are needed to evaluate FIASMAs in SARS-CoV-2 infection as off-label therapy.

Novel approach for attenuation of pirfenidone-induced digestive symptoms: a respirable powder formulation of pirfenidone

Aim: Orally taken pirfenidone (PFD) often causes digestive symptoms. A respirable powder formulation of PFD (PFD-RP) was previously developed, and this study aimed to verify the risk of digestive symptoms after insufflation of PFD-RP. Materials & methods: Intestinal motility and gastrointestinal exposure levels was evaluated in PFD-RP (0.3-mg PFD/rat: a pharmacologically effective dose) and orally taken PFD (10-100 mg/kg) groups. Results & conclusion: Orally taken PFD at doses above 30 mg/kg significantly inhibited intestinal motility. In contrast, insufflated PFD-RP led to comparable intestinal motility in control group, and gastrointestinal exposure levels in PFD-RP group were markedly lower than those in orally taken PFD groups. Inhalation therapy using PFD-RP may be efficacious to reduce the risk of digestive symptoms frequently induced by orally taken PFD.

Sleep in Older Adults and Its Possible Relations With COVID-19

Since the beginning of the COVID-19 pandemic, older adults have been found to be a highly vulnerable group, with a higher prevalence of severe cases and negative outcomes. Research has focused on the reasons why older adults are at greater risk; Sleep-related factors have been suggested as one possible explanation for this. An individual's sleep pattern undergoes significant changes over the course of their life. In older adults a specific sleep profile can be observed, one characterized by advanced sleep timing, a morningness preference, longer sleep-onset latency, shorter overall sleep duration, increased sleep fragmentation, reduced slow-wave sleep and, increased wake time after sleep onset. Additionally, an increased prevalence of sleep disorders can be observed, such as obstructive sleep apnea and insomnia. Previous research has already linked sleep disorders (especially sleep apnea) with COVID-19, but few studies have focused specifically on the older population. We believe that the intrinsic sleep patterns of older adults, and the prevalence of sleep disorders in this population, may be important factors that could explain why they are at a greater risk of negative COVID-19 outcomes. In this review, we discuss the relationship between sleep and COVID-19 among older adults, focusing on three different aspects: (1) Sleep-related issues that might increase the likelihood of getting infected by SARS-COV-2; (2) Sleep disturbances that might increase the predisposition to worse COVID-19 prognosis and outcomes; and (3) COVID-19-related aspects affecting community-dwelling older adults, such as social isolation, quarantine, and home confinement, among others, that might impact sleep.

Melatonin for the Early Treatment of COVID-19: A Narrative Review of Current Evidence and Possible Efficacy

OBJECTIVE

To discuss the use of melatonin as an early treatment option on the first day of diagnosis for COVID-19.

METHODS

Medical Subject Headings terms "COVID-19" and "viral diseases" were manually searched on PubMed, and relevant articles were included.

RESULTS

The results showed that melatonin acts to reduce reactive oxygen species-mediated damage, cytokine-induced inflammation, and lymphopenia in viral diseases similar to COVID-19.

CONCLUSION

These conclusions provide evidence for potential benefits in melatonin use for COVID-19 treatment as early as the day of diagnosis.

Role of Serine Proteases and Host Cell Receptors Involved in Proteolytic Activation, Entry of SARS-CoV-2 and Its Current Therapeutic Options

The current global pandemic of a novel severe acute respiratory syndrome coronavirus-2 continues with its public health disaster beginning from late December 2019 in Wuhan, Hubei province, China. The scientific community has tried to fight against this novel coronavirus through vaccine development and designing different candidate drugs. However, there is no well-defined therapy to prevent 2019-nCov infection, thus complete prevention of the virus remains difficult. Therefore, it is a critical factor for death of millions worldwide. Many clinical trials and insights are ongoing in the struggle with this pandemic of SARS-CoV-2. SARS-CoV-2 entry into the host cell requires host cell angiotensin-converting enzyme-2 (ACE2) and glucose regulated protein 78 (GRP78). On the other hand, proteolytic activation of the viral spike protein (S protein) needs the host cell serine proteases, including transmembrane serine protease 2 (TMPRSS2), cathepsins, trypsin and furin. This review focuses on the protein involved in the mechanism of entry, and proteolytic activation. In addition, it looks at current therapeutic options for SARS-CoV-2.

Artificial Intelligence for Clinical Decision Support in Sepsis

Sepsis is one of the main causes of death in critically ill patients. Despite the continuous development of medical technology in recent years, its morbidity and mortality are still high. This is mainly related to the delay in starting treatment and non-adherence of clinical guidelines. Artificial intelligence (AI) is an evolving field in medicine, which has been used to develop a variety of innovative Clinical Decision Support Systems. It has shown great potential in predicting the clinical condition of patients and assisting in clinical decision-making. AI-derived algorithms can be applied to multiple stages of sepsis, such as early prediction, prognosis assessment, mortality prediction, and optimal management. This review describes the latest literature on AI for clinical decision support in sepsis, and outlines the application of AI in the prediction, diagnosis, subphenotyping, prognosis assessment, and clinical management of sepsis. In addition, we discussed the challenges of implementing and accepting this non-traditional methodology for clinical purposes.

Is PF-00835231 a Pan-SARS-CoV-2 Mpro Inhibitor? A Comparative Study

The COVID-19 outbreak continues to spread worldwide at a rapid rate. Currently, the absence of any effective antiviral treatment is the major concern for the global population. The reports of the occurrence of various point mutations within the important therapeutic target protein of SARS-CoV-2 has elevated the problem. The SARS-CoV-2 main protease (Mpro) is a major therapeutic target for new antiviral designs. In this study, the efficacy of PF-00835231 was investigated (a Mpro inhibitor under clinical trials) against the Mpro and their reported mutants. Various in silico approaches were used to investigate and compare the efficacy of PF-00835231 and five drugs previously documented to inhibit the Mpro. Our study shows that PF-00835231 is not only effective against the wild type but demonstrates a high affinity against the studied mutants as well.

Drug Repurposing in the COVID-19 Era: Insights from Case Studies Showing Pharmaceutical Peculiarities

COVID-19 may lead to severe respiratory distress syndrome and high risk of death in some patients. So far (January 2021), only the antiviral remdesivir has been approved, although no significant benefits in terms of mortality and clinical improvement were recently reported. In a setting where effective and safe treatments for COVID-19 are urgently needed, drug repurposing may take advantage of the fact that the safety profile of an agent is already well known and allows rapid investigation of the efficacy of potential treatments, at lower costs and with reduced risk of failure. Furthermore, novel pharmaceutical formulations of older agents (e.g., aerosolized administration of chloroquine/hydroxychloroquine, remdesivir, heparin, pirfenidone) have been tested in order to increase pulmonary delivery and/or antiviral effects of potentially active drugs, thus overcoming pharmacokinetic issues. In our review, we will highlight the importance of the drug repurposing strategy in the context of COVID-19, including regulatory and ethical aspects, with a specific focus on novel pharmaceutical formulations and routes of administration.

Innate Receptor Activation Patterns Involving TLR and NLR Synergisms in COVID-19, ALI/ARDS and Sepsis Cytokine Storms: A Review and Model Making Novel Predictions and Therapeutic Suggestions

Severe COVID-19 is characterized by a “cytokine storm”, the mechanism of which is not yet understood. I propose that cytokine storms result from synergistic interactions among Toll-like receptors (TLR) and nucleotide-binding oligomerization domain-like receptors (NLR) due to combined infections of SARS-CoV-2 with other microbes, mainly bacterial and fungal. This proposition is based on eight linked types of evidence and their logical connections. (1) Severe cases of COVID-19 differ from healthy controls and mild COVID-19 patients in exhibiting increased TLR4, TLR7, TLR9 and NLRP3 activity. (2) SARS-CoV-2 and related coronaviruses activate TLR3, TLR7, RIG1 and NLRP3. (3) SARS-CoV-2 cannot, therefore, account for the innate receptor activation pattern (IRAP) found in severe COVID-19 patients. (4) Severe COVID-19 also differs from its mild form in being characterized by bacterial and fungal infections. (5) Respiratory bacterial and fungal infections activate TLR2, TLR4, TLR9 and NLRP3. (6) A combination of SARS-CoV-2 with bacterial/fungal coinfections accounts for the IRAP found in severe COVID-19 and why it differs from mild cases. (7) Notably, TLR7 (viral) and TLR4 (bacterial/fungal) synergize, TLR9 and TLR4 (both bacterial/fungal) synergize and TLR2 and TLR4 (both bacterial/fungal) synergize with NLRP3 (viral and bacterial). (8) Thus, a SARS-CoV-2-bacterium/fungus coinfection produces synergistic innate activation, resulting in the hyperinflammation characteristic of a cytokine storm. Unique clinical, experimental and therapeutic predictions (such as why melatonin is effective in treating COVID-19) are discussed, and broader implications are outlined for understanding why other syndromes such as acute lung injury, acute respiratory distress syndrome and sepsis display varied cytokine storm symptoms.

Effect of Leptin on Chronic Inflammatory Disorders: Insights to Therapeutic Target to Prevent Further Cardiovascular Complication

In response to obesity-associated chronic inflammatory disorders, adipose tissue releases a biologically active peptide known as leptin. Leptin activates the secretion of chemical mediators, which contribute to the pathogenesis of chronic inflammatory disorders, such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and psoriasis. Conversely, adiposity and obesity are the major aggravating risk factors in the pathogenesis of metabolic syndrome (MetS), including type II diabetes mellitus and obesity-associated hypertension. Elevated level of leptin in obesity-associated hypertension causes an increase in the production of aldosterone, which also results in elevation of arterial blood pressure. Hyperleptinemia is associated with the progress of the atherosclerosis through secretion of pro-inflammatory cytokines, like interleukin 6 (IL-6), tumor necrosis factor α (TNF-α), IL-17, and other cytokines to promote inflammation. The release of those cytokines leads to chronic inflammatory disorders and obesity-associated MetS. Thus, the aberrant leptin level in both MetS and chronic inflammatory disorders also leads to the complication of cardiovascular diseases (CVD). Therapeutic target of leptin regarding its pro-inflammatory effect and dysregulated sympathetic nervous system activity may prevent further cardiovascular complication. This review mainly assesses the mechanism of leptin on the pathogenesis and further cardiovascular risk complication of chronic inflammatory disorders.