Drug repositioning for psychiatric and neurological disorders through a network medicine approach

Dysregulated Pathways During Pregnancy Predict Drug Candidates in Neurodevelopmental Disorders

Maternal health during pregnancy has a direct impact on the risk and severity of neurodevelopmental disorders (NDDs) in the offspring, especially in the case of drug exposure. However, little progress has been made to assess the risk of drug exposure during pregnancy due to ethical constraints and drug use factors. We collected and manually curated sub-pathways and pathways (sub-/pathways) and drug information to propose an analytical framework for predicting drug candidates. This framework linked sub-/pathway activity and drug response scores derived from gene transcription data and was applied to human fetal brain development and six NDDs. Further, specific and pleiotropic sub-/pathways/drugs were identified using entropy, and sex bias was analyzed in conjunction with logistic regression and random forest models. We identified 19 disorder-associated and 256 regionally pleiotropic and specific candidate drugs that targeted risk sub-/pathways in NDDs, showing temporal or spatial changes across fetal development. Moreover, 5443 differential drug-sub-/pathways exhibited sex-biased differences after filling in the gender labels. A user-friendly NDDP visualization website ( https://ndd-lab.shinyapps.io/NDDP ) was developed to allow researchers and clinicians to access and retrieve data easily. Our framework overcame data gaps and identified numerous pleiotropic and specific candidates across six disorders and fetal developmental trajectories. This could significantly contribute to drug discovery during pregnancy and can be applied to a wide range of traits.

Integrated bioinformatics and interaction analysis to advance chronotherapies for mental disorders

Introduction

Robust connections have been identified between the pathophysiology of mental disorders and the functioning of the circadian system. The overarching objective of this study was to investigate the potential for circadian rhythms to be leveraged for therapeutics in mental disorders.

Methods

We considered two approaches to chronotherapy-optimal timing of existing medications ("clocking the drugs") and redressing circadian abnormalities with small molecules ("drugging the clock"). We assessed whether circadian rhythm-modulating compounds can interact with the prominent drug targets of mental disorders utilizing computational tools like molecular docking and molecular dynamics simulation analysis.

Results

Firstly, an analysis of transcript-level rhythmic patterns in recognized drug targets for mental disorders found that 24-hour rhythmic patterns were measurable in 54.4% of targets in mice and 35.2% in humans. We also identified several drug receptors exhibiting 24-hour rhythmicity involved in critical physiological pathways for neural signaling and communication, such as neuroactive ligand-receptor interaction, calcium signaling pathway, cAMP signaling pathway, and dopaminergic and cholinergic synapses. These findings advocate that further research into the timing of drug administration in mental disorders is urgently required. We observed that many pharmacological modulators of mammalian circadian rhythms, including KL001, SR8278, SR9009, Nobiletin, and MLN4924, exhibit stable binding with psychotropic drug targets.

Discussion

These findings suggest that circadian clock-modulating pharmacologically active small molecules could be investigated further for repurposing in the treatment of mood disorders. In summary, the present analyses indicate the potential of chronotherapeutic approaches to mental disorder pharmacotherapy and specify the need for future circadian rhythm-oriented clinical research.

One path, two solutions: Network-based analysis identifies targetable pathways for the treatment of comorbid type II diabetes and neuropsychiatric disorders

Comorbid diseases complicate patient outcomes and escalate healthcare costs, necessitating the need for a deeper mechanistic understanding. Neuropsychiatric disorders (NPDs) such as Neurotic Disorder, Major Depression, Bipolar Disorder, Anxiety Disorder, and Schizophrenia significantly exacerbate Type 2 Diabetes Mellitus (DM2), often leading to suboptimal treatment outcomes. The neurobiological mechanisms underlying this comorbidity remain poorly understood. To address this gap, we developed a novel pathway-based network computational framework to identify critical shared disease mechanisms between DM2 and these five prevalent comorbid NPDs. Our approach involves reconstructing an integrated DM2 ∩ NPDs KEGG pathway-pathway network and employs two complementary analytical methods, including the "minimum path to comorbidity" method to identify the shortest path fostering comorbid development. This analysis uncovered shared pathways like the PI3K-Akt signaling pathway and highlighted key nodes such as calcium signaling, MAPK, estrogen signaling, and apoptosis pathways. Dysregulation of these pathways likely contributes to the development of DM2-NPDs comorbidity. These findings have significant clinical implications, as they identify promising therapeutic targets that could lead to more effective treatments addressing both DM2 and NPDs simultaneously. Our model not only elucidates the intricate molecular interactions driving this comorbidity but also identifies promising therapeutic targets, paving the way for innovative treatment strategies. Additionally, the framework developed in this study can be adapted to study other complex comorbid conditions, advancing personalized medicine for comorbidities and improving patient care.

Phenotypic approaches for CNS drugs

Central nervous system (CNS) drug development is plagued by high clinical failure rate. Phenotypic assays promote clinical translation of drugs by reducing complex brain diseases to measurable, clinically valid phenotypes. We critique recent platforms integrating patient-derived brain cells, which most accurately recapitulate CNS disease phenotypes, with higher throughput models, including immortalized cells, to balance validity and scalability. These platforms were screened with conventional commercial chemogenomic compound libraries. We explore emerging library curation strategies to improve hit rate and quality, and screening novel fragment libraries as alternatives, for more tractable drug target deconvolution. The clinically relevant models used in these platforms could harbor important, unidentified drug targets, so we review evolving agnostic target deconvolution approaches, including chemical proteomics and artificial intelligence (AI), which aid in phenotypic screening hit mechanism elucidation, thereby facilitating rational hit-to-drug optimization.

Method to link medicines to diseases using multiplex networks

The reuse of well-established medicines using computational modeling has gained a lot of attention due to its tremendous benefits. Based on this perspective, a new method for linking known medicines to diseases is proposed. The creation of a new treatment or medicine can be financially and temporally costly and the reuse of medicines is one possibility to accelerate this process efficiently. The main purpose of the reuse of medicines is to reduce some stages of the development of new medicines, motivating the proposition of several methods nowadays. In this work, a new method is developed aiming to connect known medicines to diseases based on available networks of protein interactions and available lists of medicines that affect protein action. The concepts of multiplex networks are used to connect subgraphs of vertices that represent medicines and proteins. The core of the procedure is determined by a weighting strategy constructed to define precisely the more relevant connections. The method was compared to other network link methods in the literature and a case study was presented and evaluated by the proposed method.

NMDA glutamate receptor antagonist MK-801 induces proteome changes in adult human brain slices which are partially counteracted by haloperidol and clozapine

Deciphering the molecular pathways associated with N-methyl-D-aspartate receptor (NMDAr) hypofunction and its interaction with antipsychotics is necessary to advance our understanding of the basis of schizophrenia, as well as our capacity to treat this disease. In this regard, the development of human brain-derived models that are amenable to studying the neurobiology of schizophrenia may contribute to filling the gaps left by the widely employed animal models. Here, we assessed the proteomic changes induced by the NMDA glutamate receptor antagonist MK-801 on human brain slice cultures obtained from adult donors submitted to respective neurosurgery. Initially, we demonstrated that MK-801 diminishes NMDA glutamate receptor signaling in human brain slices in culture. Next, using mass-spectrometry-based proteomics and systems biology in silico analyses, we found that MK-801 led to alterations in proteins related to several pathways previously associated with schizophrenia pathophysiology, including ephrin, opioid, melatonin, sirtuin signaling, interleukin 8, endocannabinoid, and synaptic vesicle cycle. We also evaluated the impact of both typical and atypical antipsychotics on MK-801-induced proteome changes. Interestingly, the atypical antipsychotic clozapine showed a more significant capacity to counteract the protein alterations induced by NMDAr hypofunction than haloperidol. Finally, using our dataset, we identified potential modulators of the MK-801-induced proteome changes, which may be considered promising targets to treat NMDAr hypofunction in schizophrenia. This dataset is publicly available and may be helpful in further studies aimed at evaluating the effects of MK-801 and antipsychotics in the human brain.

A Profound Insight into the Structure-activity Relationship of Ubiquitous Scaffold Piperazine: An Explicative Review

Despite extensive research in the field of drug discovery and development, still there is a need to develop novel molecular entities. Literature reveals a substantial heterocyclic nucleus named, piperazine, which shows an immense therapeutic voyage. For several decades, molecules having the piperazine nucleus have entered the market as a drug exhibiting biological potential. It was known to possess antipsychotic, antihistamine, antianginal, antidepressant, anticancer, antiviral, cardioprotective, and anti-inflammatory activity with a specific basis for structural activity relationship. Thus, it is regarded as a key structural feature in most of the already available therapeutic drugs in the market. Reports also suggest that the extensive utilization of these currently available drugs having a piperazine nucleus shows increasing tolerance significantly day by day. In addition to this, various other factors like solubility, low bioavailability, cost-effectiveness, and imbalance between pharmacokinetics and pharmacodynamics profile limit their utilization. Focusing on that issues, various structural modification studies were performed on the piperazine moiety to develop new derivatives/analogs to overcome the problems associated with available marketed drugs. Thus, this review article aims to gain insight into the number of structural modifications at the N-1 and N-4 positions of the piperazine scaffold. This SAR approach may prove to be the best way to overcome the above-discussed drawbacks and lead to the design of drug molecules with better efficacy and affinity. Hence, there is an urgent need to focus on the structural features of this scaffold which paves further work for deeper exploration and may help medicinal chemists as well as pharmaceutical industries.

Augmentation Therapies as Treatments for Coexisting Somatic Problems in Schizophrenia-A Systematic Review

The aim of this review is to appraise the data from available randomized clinical trials (RCT) regarding the possible combinations of neuroleptic and non-antipsychotic treatment which could enhance antipsychotic therapy efficacy whilst simultaneously addressing somatic symptoms in individuals with schizophrenia. A systematic search of the PubMed database up to February 2022 was conducted. Inclusion criteria: randomized controlled trials using augmentation therapy in chronic schizophrenia in adults, written in English, and only studies with psychometric assessments of schizophrenia were incorporated. Exclusion criteria: non-clinical, first episode of schizophrenia, patients on medication other than antipsychotics augmented, and not adjunctive therapy. Overall, 37 studies of 1931 patients with schizophrenia who received a combination of antipsychotic medication with other drugs were selected. A statistically significant reduction of negative and positive symptoms of schizophrenia, measured with the PANSS scale, when using a combination of antipsychotic treatment along with aspirin, simvastatin, N-acetylcysteine, or pioglitazone was found. A combination of antipsychotic medication with aspirin, simvastatin, N-acetylcysteine, or pioglitazone seems to be effective in the reduction of symptoms of schizophrenia in adults, but long-term studies are required to confirm this effect.

Novel therapeutic avenues for the study of chronic liver disease and regeneration: The foundation of the Iberoamerican Consortium for the study of liver Cirrhosis

Unfortunately, there is a gap of understanding in the pathophysiology of chronic liver disease due to the lack of experimental models that exactly mimic the human disease. Additionally, the diagnosis of patients is very poor due to the lack of biomarkers than can detect the disease in early stages. Thus, it is of utmost interest the generation of a multidisciplinary consortium from different countries with a direct translation. The present reports the meeting of the 2021 Iberoamerican Consortium for the study of liver Cirrhosis, held online, in October 2021. The meeting, was focused on the recent advancements in the field of chronic liver disease and cirrhosis with a specific focus on cell pathobiology and liver regeneration, molecular and cellular targets involved in non-alcoholic hepatic steatohepatitis, alcoholic liver disease (ALD), both ALD and western diet, and end-stage liver cirrhosis and hepatocellular carcinoma. In addition, the meeting highlighted recent advances in targeted novel technology (-omics) and opening therapeutic avenues in this field of research.

Artificial Intelligence for Dementia Research Methods Optimization

INTRODUCTION

Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater.

METHODS

We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research.

RESULTS

We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future.

DISCUSSION

ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.

Levetiracetam Attenuates Adolescent Stress-induced Behavioral and Electrophysiological Changes Associated With Schizophrenia in Adult Rats

BACKGROUND AND HYPOTHESIS

Stress during adolescence is a major risk factor for schizophrenia. We have found previously in rats that adolescent stress caused, in adulthood, behavioral changes and enhanced ventral tegmental area (VTA) dopamine system activity, which were associated with dysregulation of the excitatory-inhibitory (E/I) balance in the ventral hippocampus (vHip). Levetiracetam, an anticonvulsant drug, regulates the release of neurotransmitters, including glutamate, via SV2A inhibition. It also modulates parvalbumin interneuron activity via Kv3.1 channels. Therefore, levetiracetam could ameliorate deficits in the E/I balance. We tested whether levetiracetam attenuate the adolescent stress-induced behavioral changes, vHip hyperactivity, and enhanced VTA dopamine system activity in adult rats.

STUDY DESIGN

Male Sprague-Dawley rats were subjected to a combination of daily footshock (postnatal day [PD] 31-40), and three 1 h-restraint stress sessions (at PD31, 32, and 40). In adulthood (PD62), animals were tested for anxiety responses (elevated plus-maze and light-dark box), social interaction, and cognitive function (novel object recognition test). The activity of vHip pyramidal neurons and VTA dopamine neurons was also recorded.

STUDY RESULTS

Adolescent stress produced anxiety-like responses and impaired sociability and cognitive function. Levetiracetam (10 mg/kg) reversed these changes. Levetiracetam also reversed the increased VTA dopamine neuron population activity and the enhanced firing rate of vHip pyramidal neurons induced by adolescent stress.

CONCLUSIONS

These findings suggest that levetiracetam attenuates the adverse outcomes associated with schizophrenia caused by stress during adolescence.

Collaboration and knowledge integration for successful brain therapeutics - lessons learned from the pandemic

Brain diseases are a major cause of death and disability worldwide and contribute significantly to years of potential life lost. Although there have been considerable advances in biological mechanisms associated with brain disorders as well as drug discovery paradigms in recent years, these have not been sufficiently translated into effective treatments. This Special Article expands on Keystone Symposia's pre- and post-pandemic panel discussions on translational neuroscience research. In the article, we discuss how lessons learned from the COVID-19 pandemic can catalyze critical progress in translational research, with efficient collaboration bridging the gap between basic discovery and clinical application. To achieve this, we must place patients at the center of the research paradigm. Furthermore, we need commitment from all collaborators to jointly mitigate the risk associated with the research process. This will require support from investors, the public sector and pharmaceutical companies to translate disease mechanisms into world-class drugs. We also discuss the role of scientific publishing in supporting these models of open innovation. Open science journals can now function as hubs to accelerate progress from discovery to treatments, in neuroscience in particular, making this process less tortuous by bringing scientists together and enabling them to exchange data, tools and knowledge effectively. As stakeholders from a broad range of scientific professions, we feel an urgency to advance brain disease therapies and encourage readers to work together in tackling this challenge.

Therapeutics of Alzheimer's Disease: Recent Developments

With increasing aging, dementia is a growing public health concern globally. Patients with dementia have multiple psychological and behavioral changes, including depression, anxiety, inappropriate behavior, paranoia, agitation, and hallucinations. The major types of dementia are Alzheimer's disease (AD), vascular dementia (VCID), Lewy body dementia (LBD), frontotemporal dementia (FTD), and mixed dementia (MiAD). Among these, AD is the most common form of dementia in the elderly population. In the last three decades, tremendous progress has been made in understanding AD's biology and disease progression, particularly its molecular basis, biomarker development, and drug discovery. Multiple cellular changes have been implicated in the progression of AD, including amyloid beta, phosphorylated tau, synaptic damage, mitochondrial dysfunction, deregulated microRNAs, inflammatory changes, hormonal deregulation, and others; based on these changes, therapeutic strategies have been developed, which are currently being tested in animal models and human clinical trials. The purpose of our article is to highlight recent therapeutic strategies' developments, critically discuss current strategies' failures, and propose new strategies to combat this devasting mental illness.

Positional scanning of natural product hispidol's ring-B: discovery of highly selective human monoamine oxidase-B inhibitor analogues downregulating neuroinflammation for management of neurodegenerative diseases

Multifunctional molecules might offer better treatment of complex multifactorial neurological diseases. Monoaminergic pathways dysregulation and neuroinflammation are common convergence points in diverse neurodegenerative and neuropsychiatric disorders. Aiming to target these diseases, polypharmacological agents modulating both monoaminergic pathways and neuroinflammatory were addressed. A library of analogues of the natural product hispidol was prepared and evaluated for inhibition of monoamine oxidases (MAOs) isoforms. Several molecules emerged as selective potential MAO B inhibitors. The most promising compounds were further evaluated in vitro for their impact on microglia viability, induced production of proinflammatory mediators and MAO-B inhibition mechanism. Amongst tested compounds, 1p was a safe potent competitive reversible MAO-B inhibitor and inhibitor of microglial production of neuroinflammatory mediators; NO and PGE2. In-silico study provided insights into molecular basis of the observed selective MAO B inhibition. This study presents compound 1p as a promising lead compound for management of neurodegenerative disease.

Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders

Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.

A global picture: therapeutic perspectives for COVID-19

The COVID-19 pandemic is a lethal virus outbreak by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which has severely affected human lives and the global economy. The most vital part of the research and development of therapeutic agents is to design drug products to manage COVID-19 efficiently. Numerous attempts have been in place to determine the optimal drug dose and combination of drugs to treat the disease on a global scale. This article documents the information available on SARS-CoV-2 and its life cycle, which will aid in the development of the potential treatment options. A consolidated summary of several natural and repurposed drugs to manage COVID-19 is depicted with summary of current vaccine development. People with high age, comorbity and concomitant illnesses such as overweight, metabolic disorders, pulmonary disease, coronary heart disease, renal failure, fatty liver and neoplastic disorders are more prone to create serious COVID-19 and its consequences. This article also presents an overview of post-COVID-19 complications in patients.

A global picture: therapeutic perspectives for COVID-19

The COVID-19 pandemic is a lethal virus outbreak by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which has severely affected human lives and the global economy. The most vital part of the research and development of therapeutic agents is to design drug products to manage COVID-19 efficiently. Numerous attempts have been in place to determine the optimal drug dose and combination of drugs to treat the disease on a global scale. This article documents the information available on SARS-CoV-2 and its life cycle, which will aid in the development of the potential treatment options. A consolidated summary of several natural and repurposed drugs to manage COVID-19 is depicted with summary of current vaccine development. People with high age, comorbity and concomitant illnesses such as overweight, metabolic disorders, pulmonary disease, coronary heart disease, renal failure, fatty liver and neoplastic disorders are more prone to create serious COVID-19 and its consequences. This article also presents an overview of post-COVID-19 complications in patients.

The evolution of knowledge on genes associated with human diseases

Thousands of biomedical scientific articles, including those describing genes associated with human diseases, are published every week. Computational methods such as text mining and machine learning algorithms are now able to automatically detect these associations. In this study, we used a cognitive computing text-mining application to construct a knowledge network comprising 3,723 genes and 99 diseases. We then tracked the yearly changes on these networks to analyze how our knowledge has evolved in the past 30 years. Our systems approach helped to unravel the molecular bases of diseases and detect shared mechanisms between clinically distinct diseases. It also revealed that multi-purpose therapeutic drugs target genes that are commonly associated with several psychiatric, inflammatory, or infectious disorders. By navigating this knowledge tsunami, we were able to extract relevant biological information and insights about human diseases.

Precision Psychiatry: Machine learning as a tool to find new pharmacological targets

There is an increasing amount of data arising from neurobehavioral sciences and medical records that cannot be adequately analyzed by traditional research methods. New drugs develop at a slow rate and seem unsatisfactory for the majority of neurobehavioral disorders. Machine learning (ML) techniques, instead, can incorporate psychopathological, computational, cognitive, and neurobiological underpinning knowledge leading to a refinement of detection, diagnosis, prognosis, treatment, research, and support. Machine and deep learning methods are currently used to accelerate the process of discovering new pharmacological targets and drugs.

OBJECTIVE

The present work reviews current evidence regarding the contribution of machine learning to the discovery of new drug targets.

METHODS

Scientific articles from PubMed, SCOPUS, EMBASE, and Web of Science Core Collection published until May 2021 were included in this review.

RESULTS

The most significant areas of research are schizophrenia, depression and anxiety, Alzheimer´s disease, and substance use disorders. ML techniques have pinpointed target gene candidates and pathways, new molecular substances, and several biomarkers regarding psychiatric disorders. Drug repositioning studies using ML have identified multiple drug candidates as promising therapeutic agents.

CONCLUSION

Next-generation ML techniques and subsequent deep learning may power new findings regarding the discovery of new pharmacological agents by bridging the gap between biological data and chemical drug information.

Fenfluramine as antiseizure medication for epilepsy

Fenfluramine hydrochloride has classically been described as acting pharmacologically through a serotonergic mechanism. Therefore, it was initially used as an anorectic drug, given that impaired serotonin homeostasis may be associated with increased food intake. Although positive results were documented, cardiovascular concerns resulted in its temporary withdrawal. Nevertheless, a novel role in patients with epilepsy was later suggested by isolated clinical observations. The wide application of genetic testing allowed the classification (predominantly as Dravet syndrome) of patients in whom benefit was seen, while with the development of zebrafish models, its antiepileptic properties were confirmed at a molecular level. Data from randomized clinical trials have shown a beneficial effect of fenfluramine, as an adjunct therapy, on seizure control for children with Dravet syndrome, though there is still uncertainty about the impact on neurodevelopment in these patients. No signs of heart valve disease have been documented to date. Long-term and appropriately designed clinical studies will verify whether fenfluramine is a therapeutic agent of high importance, living up to the promise shown so far. What this paper adds Fenfluramine is a very promising repurposed therapy specifically for seizures in Dravet syndrome. The long-term effect of fenfluramine on neurodevelopmental prognosis requires further investigation.

Network vaccinology

The structure and function of the immune system is governed by complex networks of interactions between cells and molecular components. Vaccination perturbs these networks, triggering specific pathways to induce cellular and humoral immunity. Systems vaccinology studies have generated vast data sets describing the genes related to vaccination, motivating the use of new approaches to identify patterns within the data. Here, we describe a framework called Network Vaccinology to explore the structure and function of biological networks responsible for vaccine-induced immunity. We demonstrate how the principles of graph theory can be used to identify modules of genes, proteins, and metabolites that are associated with innate and adaptive immune responses. Network vaccinology can be used to assess specific and shared molecular mechanisms of different types of vaccines, adjuvants, and routes of administration to direct rational design of the next generation of vaccines.

Drug targets for COVID-19 therapeutics: Ongoing global efforts

The current global pandemic COVID-19 caused by the SARS-CoV-2 virus has already inflicted insurmountable damage both to the human lives and global economy. There is an immediate need for identification of effective drugs to contain the disastrous virus outbreak. Global efforts are already underway at a war footing to identify the best drug combination to address the disease. In this review, an attempt has been made to understand the SARS-CoV-2 life cycle, and based on this information potential druggable targets against SARS-CoV-2 are summarized. Also, the strategies for ongoing and future drug discovery against the SARS-CoV-2 virus are outlined. Given the urgency to find a definitive cure, ongoing drug repurposing efforts being carried out by various organizations are also described. The unprecedented crisis requires extraordinary efforts from the scientific community to effectively address the issue and prevent further loss of human lives and health.