Project IDentif.AI: Harnessing Artificial Intelligence to Rapidly Optimize Combination Therapy Development for Infectious Disease Intervention

Flash optimization of drug combinations for Acinetobacter baumannii with IDentif.AI-AMR

Antimicrobial resistance (AMR) is an emerging threat to global public health. Specifically, Acinetobacter baumannii (A. baumannii), one of the main pathogens driving the rise of nosocomial infections, is a Gram-negative bacillus that displays intrinsic resistance mechanisms and can also develop resistance by acquiring AMR genes from other bacteria. More importantly, it is resistant to nearly 90% of standard of care (SOC) antimicrobial treatments, resulting in unsatisfactory clinical outcomes and a high infection-associated mortality rate of over 30%. Currently, there is a growing challenge to sustainably develop novel antimicrobials in this ever-expanding arms race against AMR. Therefore, a sustainable workflow that properly manages healthcare resources to ultra-rapidly design optimal drug combinations for effective treatment is needed. In this study, the IDentif.AI-AMR platform was harnessed to pinpoint effective regimens against four A. baumannii clinical isolates from a pool of nine US FDA-approved drugs. Notably, IDentif.AI-pinpointed ampicillin-sulbactam/cefiderocol and cefiderocol/polymyxin B/rifampicin combinations were able to achieve 93.89 ± 5.95% and 92.23 ± 11.89% inhibition against the bacteria, respectively, and they may diversify the reservoir of treatment options for the indication. In addition, polymyxin B in combination with rifampicin exhibited broadly applicable efficacy and strong synergy across all tested clinical isolates, representing a potential treatment strategy for A. baumannii. IDentif.AI-pinpointed combinations may potentially serve as alternative treatment strategies for A. baumannii.

The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance

Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes to address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, and epidemiological data for predicting AMR outbreaks. Although AI/ML are relatively new fields, numerous case studies offer substantial evidence of their successful application in predicting AMR outbreaks with greater accuracy. These models can provide insights into the discovery of novel antimicrobials, the repurposing of existing drugs, and combination therapy through the analysis of their molecular structures. In addition, AI-based clinical decision support systems in real-time guide healthcare professionals to improve prescribing of antibiotics. The review also outlines how can AI improve AMR surveillance, analyze resistance trends, and enable early outbreak identification. Challenges, such as ethical considerations, data privacy, and model biases exist, however, the continuous development of novel methodologies enables AI/ML to play a significant role in combating AMR.

Identifying Drug Sensitivities in Fission Yeast by Assessing Growth Kinetics

Kinetic assays can detect growth response to changing conditions in fission yeast. This may allow the use of fission yeast in drug library screens. Building upon our other work to examine half-maximal inhibitory concentrations, kinetic curves show direct response of cell populations to varying drug concentrations and conditions. The slope is useful to assess growth rate. Absolute growth height can be used to understand overall culture response and compare between doses or strains. This can be paired with a metabolic screening reagent, CCK8, which detects dehydrogenase activity as a proxy for proliferation. These methods assess time-based changes in fission yeast health and provide inexpensive and high-content alternatives to colony-forming assays.

Combination therapy synergism prediction for virus treatment using machine learning models

Combining different drugs synergistically is an essential aspect of developing effective treatments. Although there is a plethora of research on computational prediction for new combination therapies, there is limited to no research on combination therapies in the treatment of viral diseases. This paper proposes AI-based models for predicting novel antiviral combinations to treat virus diseases synergistically. To do this, we assembled a comprehensive dataset comprising information on viral strains, drug compounds, and their known interactions. As far as we know, this is the first dataset and learning model on combination therapy for viruses. Our proposal includes using a random forest model, an SVM model, and a deep model to train viral combination therapy. The machine learning models showed the highest performance, and the predicted values were validated by a t-test, indicating the effectiveness of the proposed methods. One of the predicted combinations of acyclovir and ribavirin has been experimentally confirmed to have a synergistic antiviral effect against herpes simplex type-1 virus, as described in the literature.

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well-known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI-based models for drug-target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.

N-of-1 medicine

ABSTRACT

The fields of precision and personalised medicine have led to promising advances in tailoring treatment to individual patients. Examples include genome/molecular alteration-guided drug selection, single-patient gene therapy design and synergy-based drug combination development, and these approaches can yield substantially diverse recommendations. Therefore, it is important to define each domain and delineate their commonalities and differences in an effort to develop novel clinical trial designs, streamline workflow development, rethink regulatory considerations, create value in healthcare and economics assessments, and other factors. These and other segments are essential to recognise the diversity within these domains to accelerate their respective workflows towards practice-changing healthcare. To emphasise these points, this article elaborates on the concept of digital health and digital medicine-enabled N-of-1 medicine, which individualises combination regimen and dosing using a patient's own data. We will conclude with recommendations for consideration when developing novel workflows based on emerging digital-based platforms.

Drug Repurposing in the Chemotherapy of Infectious Diseases

Repurposing is a universal mechanism for innovation, from the evolution of feathers to the invention of Velcro tape. Repurposing is particularly attractive for drug development, given that it costs more than a billion dollars and takes longer than ten years to make a new drug from scratch. The COVID-19 pandemic has triggered a large number of drug repurposing activities. At the same time, it has highlighted potential pitfalls, in particular when concessions are made to the target product profile. Here, we discuss the pros and cons of drug repurposing for infectious diseases and analyze different ways of repurposing. We distinguish between opportunistic and rational approaches, i.e., just saving time and money by screening compounds that are already approved versus repurposing based on a particular target that is common to different pathogens. The latter can be further distinguished into divergent and convergent: points of attack that are divergent share common ancestry (e.g., prokaryotic targets in the apicoplast of malaria parasites), whereas those that are convergent arise from a shared lifestyle (e.g., the susceptibility of bacteria, parasites, and tumor cells to antifolates due to their high rate of DNA synthesis). We illustrate how such different scenarios can be capitalized on by using examples of drugs that have been repurposed to, from, or within the field of anti-infective chemotherapy.

Nanocomposite fibers based on cellulose acetate loaded with fullerene for cancer therapy: preparation, characterization and in-vitro evaluation

The current prevalence of cancerous diseases necessitates the exploration of materials that can effectively treat these conditions while minimizing the occurrence of adverse side effects. This study aims to identify materials with the potential to inhibit the metastasis of cancerous diseases within the human body while concurrently serving as therapeutic agents for their treatment. A novel approach was employed to enhance the anti-cancer properties of electrospun cellulose fibers by incorporating fullerene nanoparticles (NPs) into cellulose acetate (CA) fibers, resulting in a composite material called Fullerene@CA. This development aimed at utilizing the anti-cancer properties of fullerenes for potential therapeutic applications. This process has been demonstrated in vitro against various types of cancer, and it was found that Fullerene@CA nanocomposite fibers displayed robust anticancer activity. Cancer cells (Caco-2, MDA-MB 231, and HepG-2 cells) were inhibited by 0.3 and 0.5 mg.g-1 fullerene doses by 58.62-62.87%, 47.86-56.43%, and 48.60-57.73%, respectively. The tested cancer cells shrink and lose their spindle shape due to morphological changes. The investigation of the prepared nanocomposite reveals its impact on various genes, such as BCL2, NF-KB, p53, Bax, and p21, highlighting the therapeutic compounds' effectiveness. The experimental results demonstrated that the incorporation of NPs into CA fibers resulted in a significant improvement in their anti-cancer efficacy. Therefore, it is suggested that these modified fibers could be utilized as a novel therapeutic approach for the treatment and prevention of cancer metastasis.

Current state and prospects of artificial intelligence in allergy

The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.

Artificial intelligence (AI) in Monkeypox infection prevention

Monkeypox is a possible public health concern that requires appropriate attention in order to prevent the spread of the disease. Currently, artificial intelligence (AI) is making a significant impact on precision medicine, reshaping and integrating the large amount of data derived from multiomics analyses and revolutionizing the deep-learning strategies. There has been a significant progress in the use of AI to detect, screen, diagnose, and classify diseases, characterize virus genomes, assess biomarkers for prognostic and predictive purposes, and develop follow-up strategies. Hence, it is possible to use AI for the identification of disease clusters, cases monitoring, forecasting the future outbreak, determining mortality risk, diagnosing, managing, and identifying patterns for studying disease trends. AI may also be utilized to assist gene therapy and other therapies that we are not currently able to use in healthcare. It is possible to combine pharmacology and gene therapy with regenerative medicine with the help of AI. It will directly benefit the public in overcoming fear and panic of health risks. Therefore, AI can be an effective weapon to fight against Monkeypox infection, and may prove to be an invaluable future tool in improving the clinical management of patients. Key Points: Emergence and spread of the Monkeypox virus is a new public health crisis; threatening the world. This opinion piece highlights the urgently required information for immediate delivery of solutions on controlling and monitoring the spread of Monkeypox infection through Artificial IntelligenceCommunicated by Ramaswamy H. Sarma.

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

Background

Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care.

Methods

We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data.

Results

Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health.

Conclusions

Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.

Artificial intelligence aids in development of nanomedicines for cancer management

Over the last decade, the nanomedicine has experienced unprecedented development in diagnosis and management of diseases. A number of nanomedicines have been approved in clinical use, which has demonstrated the potential value of clinical transition of nanotechnology-modified medicines from bench to bedside. The application of artificial intelligence (AI) in development of nanotechnology-based products could transform the healthcare sector by realizing acquisition and analysis of large datasets, and tailoring precision nanomedicines for cancer management. AI-enabled nanotechnology could improve the accuracy of molecular profiling and early diagnosis of patients, and optimize the design pipeline of nanomedicines by tuning the properties of nanomedicines, achieving effective drug synergy, and decreasing the nanotoxicity, thereby, enhancing the targetability, personalized dosing and treatment potency of nanomedicines. Herein, the advances in AI-enabled nanomedicines in cancer management are elaborated and their application in diagnosis, monitoring and therapy as well in precision medicine development is discussed.

Climate change and infectious disease: A prologue on multidisciplinary cooperation and predictive analytics

Climate change impacts global ecosystems at the interface of infectious disease agents and hosts and vectors for animals, humans, and plants. The climate is changing, and the impacts are complex, with multifaceted effects. In addition to connecting climate change and infectious diseases, we aim to draw attention to the challenges of working across multiple disciplines. Doing this requires concentrated efforts in a variety of areas to advance the technological state of the art and at the same time implement ideas and explain to the everyday citizen what is happening. The world's experience with COVID-19 has revealed many gaps in our past approaches to anticipating emerging infectious diseases. Most approaches to predicting outbreaks and identifying emerging microbes of major consequence have been with those causing high morbidity and mortality in humans and animals. These lagging indicators offer limited ability to prevent disease spillover and amplifications in new hosts. Leading indicators and novel approaches are more valuable and now feasible, with multidisciplinary approaches also within our grasp to provide links to disease predictions through holistic monitoring of micro and macro ecological changes. In this commentary, we describe niches for climate change and infectious diseases as well as overarching themes for the important role of collaborative team science, predictive analytics, and biosecurity. With a multidisciplinary cooperative "all call," we can enhance our ability to engage and resolve current and emerging problems.

IDentif.AI-Omicron: Harnessing an AI-Derived and Disease-Agnostic Platform to Pinpoint Combinatorial Therapies for Clinically Actionable Anti-SARS-CoV-2 Intervention

Nanomedicine-based and unmodified drug interventions to address COVID-19 have evolved over the course of the pandemic as more information is gleaned and virus variants continue to emerge. For example, some early therapies (e.g., antibodies) have experienced markedly decreased efficacy. Due to a growing concern of future drug resistant variants, current drug development strategies are seeking to find effective drug combinations. In this study, we used IDentif.AI, an artificial intelligence-derived platform, to investigate the drug-drug and drug-dose interaction space of six promising experimental or currently deployed therapies at various concentrations: EIDD-1931, YH-53, nirmatrelvir, AT-511, favipiravir, and auranofin. The drugs were tested in vitro against a live B.1.1.529 (Omicron) virus first in monotherapy and then in 50 strategic combinations designed to interrogate the interaction space of 729 possible combinations. Key findings and interactions were then further explored and validated in an additional experimental round using an expanded concentration range. Overall, we found that few of the tested drugs showed moderate efficacy as monotherapies in the actionable concentration range, but combinatorial drug testing revealed significant dose-dependent drug-drug interactions, specifically between EIDD-1931 and YH-53, as well as nirmatrelvir and YH-53. Checkerboard validation analysis confirmed these synergistic interactions and also identified an interaction between EIDD-1931 and favipiravir in an expanded range. Based on the platform nature of IDentif.AI, these findings may support further explorations of the dose-dependent drug interactions between different drug classes in further pre-clinical and clinical trials as possible combinatorial therapies consisting of unmodified and nanomedicine-enabled drugs, to combat current and future COVID-19 strains and other emerging pathogens.

COVID-19 and artificial intelligence: Experts and dermatologists perspective

INTRODUCTION

Artificial intelligence (AI) has an important role to play in future healthcare offerings. Machine learning and artificial neural networks are subsets of AI that refer to the incorporation of human intelligence into computers to think and behave like humans.

OBJECTIVE

The objective of this review article is to discuss perspectives on the AI in relation to Coronavirus disease (COVID-19).

METHODS

Google Scholar and PubMed databases were searched to retrieve articles related to COVID-19 and AI. The current evidence is analysed and perspectives on the usefulness of AI in COVID-19 is discussed.

RESULTS

The coronavirus pandemic has rendered the entire world immobile, crashing economies, industries, and health care. Telemedicine or tele-dermatology for dermatologists has become one of the most common solutions to tackle this crisis while adhering to social distancing for consultations. While it has not yet achieved its full potential, AI is being used to combat coronavirus disease on multiple fronts. AI has made its impact in predicting disease onset by issuing early warnings and alerts, monitoring, forecasting the spread of disease and supporting therapy. In addition, AI has helped us to build a model of a virtual protein structure and has played a role in teaching as well as social control.

CONCLUSION

Full potential of AI is yet to be realized. Expert data collection, analysis, and implementation are needed to improve this advancement.

The IDentif.AI-x pandemic readiness platform: Rapid prioritization of optimized COVID-19 combination therapy regimens

IDentif.AI-x, a clinically actionable artificial intelligence platform, was used to rapidly pinpoint and prioritize optimal combination therapies against COVID-19 by pairing a prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus and Vero E6 assay with a quadratic optimization workflow. A starting pool of 12 candidate drugs developed in collaboration with a community of infectious disease clinicians was first narrowed down to a six-drug pool and then interrogated in 50 combination regimens at three dosing levels per drug, representing 729 possible combinations. IDentif.AI-x revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived, and pinpointed a number of clinically actionable drug interactions, which were further reconfirmed in SARS-CoV-2 variants B.1.351 (Beta) and B.1.617.2 (Delta). IDentif.AI-x prioritized promising drug combinations for clinical translation and can be immediately adjusted and re-executed with a new pool of promising therapies in an actionable path towards rapidly optimizing combination therapy following pandemic emergence.

A New Hope in the Fight Against Antimicrobial Resistance with Artificial Intelligence

Recent years have witnessed the rise of artificial intelligence (AI) in antimicrobial resistance (AMR) management, implying a positive signal in the fight against antibiotic-resistant microbes. The impact of AI starts with data collection and preparation for deploying AI-driven systems, which can lay the foundation for some effective infection control strategies. Primary applications of AI include identifying potential antimicrobial molecules, rapidly testing antimicrobial susceptibility, and optimizing antibiotic combinations. Aside from their outstanding effectiveness, these applications also express high potential in narrowing the burden gap of AMR among different settings around the world. Despite these benefits, the interpretability of AI-based systems or models remains vague. Attempts to address this issue had led to two novel explanation techniques, but none have shown enough robustness or comprehensiveness to be widely applied in AI and AMR control. A multidisciplinary collaboration between the medical field and advanced technology is therefore needed to partially manage this situation and improve the AI systems' performance and their effectiveness against drug-resistant pathogens, in addition to multiple equity actions for mitigating the failure risks of AI due to a global-scale equity gap.

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.

AI & COVID-19

The COVID-19 pandemic has influenced our lives significantly since March 2020, and a number of initiatives have been put forward in order to tackle its effects, including those focused on technological solutions. In this paper, we present one of such initiatives, i.e. the CLAIRE’s taskforce on AI and COVID-19, in which Artificial Intelligence methodologies and tools are being developed to help the society contrasting the pandemic. We present the different lines of development within the taskforce, some fields in which they are used, and draw few recommendations.

Artificial Intelligence against COVID-19 Pandemic: A Comprehensive Insight

COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain, which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infectious disease, yet there are no well-defined absolute treatments available at present. Several vaccination drives using emergency use authorisation vaccines have been held across many countries; however, their long-term efficacy and side-effects studies are yet to be studied. Various analytical and statistical models have been developed, however, their outcome rate is prolonged. Thus, modern science stresses the application of state-of-the-art methods to combat COVID-19. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in the battle against the COVID-19 pandemic. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e., > 95%, as reported in various studies. The extensive literature reviewed in this paper is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing. The application of Artificial Intelligence (AI) and AI-driven tools are proving to be useful in managing and fighting against the COVID-19 pandemic, especially by analysing the X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions, etc.

Fighting COVID-19 with Artificial Intelligence

The development of vaccines for the treatment of COVID-19 is paving the way for new hope. Despite this, the risk of the virus mutating into a vaccine-resistant variant still persists. As a result, the demand of efficacious drugs to treat COVID-19 is still pertinent. To this end, scientists continue to identify and repurpose marketed drugs for this new disease. Many of these drugs are currently undergoing clinical trials and, so far, only one has been officially approved by FDA. Drug repurposing is a much faster route to the clinic than standard drug development of novel molecules, nevertheless in a pandemic this process is still not fast enough to halt the spread of the virus. Artificial intelligence has already played a large part in hastening the drug discovery process, not only by facilitating the selection of potential drug candidates but also in monitoring the pandemic and enabling faster diagnosis of patients. In this chapter, we focus on the impact and challenges that artificial intelligence has demonstrated thus far with respect to drug repurposing of therapeutics for the treatment of COVID-19.

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.

Artificial Intelligence for COVID-19: A Systematic Review

Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations. Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555). Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines. Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.

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.

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 optimization of combinatorial drug therapies: Strategies and laboratorial platforms

Designing optimal combinatorial drug therapies is challenging, because the drug interactions depend not only on the drugs involved, but also on their doses. With recent advances, combinatorial drug therapy is closer than ever to clinical application. Herein, we summarize approaches and advances over the past decade for identifying and optimizing drug combination therapies, with innovations across research fields, covering physical laboratory platforms for combination screening to computational models and algorithms designed for synergism prediction and optimization. By comparing different types of approach, we detail a three-step workflow that could maximize the overall optimization efficiency, thus enabling the application of personalized optimization of combinatorial drug therapy.

Application of lung microphysiological systems to COVID-19 modeling and drug discovery: a review

There is a pressing need for effective therapeutics for coronavirus disease 2019 (COVID-19), the respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. The process of drug development is a costly and meticulously paced process, where progress is often hindered by the failure of initially promising leads. To aid this challenge, in vitro human microphysiological systems need to be refined and adapted for mechanistic studies and drug screening, thereby saving valuable time and resources during a pandemic crisis. The SARS-CoV-2 virus attacks the lung, an organ where the unique three-dimensional (3D) structure of its functional units is critical for proper respiratory function. The in vitro lung models essentially recapitulate the distinct tissue structure and the dynamic mechanical and biological interactions between different cell types. Current model systems include Transwell, organoid and organ-on-a-chip or microphysiological systems (MPSs). We review models that have direct relevance toward modeling the pathology of COVID-19, including the processes of inflammation, edema, coagulation, as well as lung immune function. We also consider the practical issues that may influence the design and fabrication of MPS. The role of lung MPS is addressed in the context of multi-organ models, and it is discussed how high-throughput screening and artificial intelligence can be integrated with lung MPS to accelerate drug development for COVID-19 and other infectious diseases.

Multi-Omics Approach in the Identification of Potential Therapeutic Biomolecule for COVID-19

COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has a disastrous effect on mankind due to the contagious and rapid nature of its spread. Although vaccines for SARS-CoV-2 have been successfully developed, the proven, effective, and specific therapeutic molecules are yet to be identified for the treatment. The repurposing of existing drugs and recognition of new medicines are continuously in progress. Efforts are being made to single out plant-based novel therapeutic compounds. As a result, some of these biomolecules are in their testing phase. During these efforts, the whole-genome sequencing of SARS-CoV-2 has given the direction to explore the omics systems and approaches to overcome this unprecedented health challenge globally. Genome, proteome, and metagenome sequence analyses have helped identify virus nature, thereby assisting in understanding the molecular mechanism, structural understanding, and disease propagation. The multi-omics approaches offer various tools and strategies for identifying potential therapeutic biomolecules for COVID-19 and exploring the plants producing biomolecules that can be used as biopharmaceutical products. This review explores the available multi-omics approaches and their scope to investigate the therapeutic promises of plant-based biomolecules in treating SARS-CoV-2 infection.

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.

Applications of artificial intelligence in battling against covid-19: A literature review

Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.

IDentif.AI: Rapidly optimizing combination therapy design against severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) with digital drug development

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to multiple drug repurposing clinical trials that have yielded largely uncertain outcomes. To overcome this challenge, we used IDentif.AI, a platform that pairs experimental validation with artificial intelligence (AI) and digital drug development to rapidly pinpoint unpredictable drug interactions and optimize infectious disease combination therapy design with clinically relevant dosages. IDentif.AI was paired with a 12-drug candidate therapy set representing over 530,000 drug combinations against the SARS-CoV-2 live virus collected from a patient sample. IDentif.AI pinpointed the optimal combination as remdesivir, ritonavir, and lopinavir, which was experimentally validated to mediate a 6.5-fold enhanced efficacy over remdesivir alone. Additionally, it showed hydroxychloroquine and azithromycin to be relatively ineffective. The study was completed within 2 weeks, with a three-order of magnitude reduction in the number of tests needed. IDentif.AI independently mirrored clinical trial outcomes to date without any data from these trials. The robustness of this digital drug development approach paired with in vitro experimentation and AI-driven optimization suggests that IDentif.AI may be clinically actionable toward current and future outbreaks.

Artificial Intelligence and technology in COVID Era: A narrative review

Application of artificial intelligence (AI) in the medical field during the coronavirus disease 2019 (COVID-19) era is being explored further due to its beneficial aspects such as self-reported data analysis, X-ray interpretation, computed tomography (CT) image recognition, and patient management. This narrative review article included published articles from MEDLINE/PubMed, Google Scholar and National Informatics Center egov mobile apps. The database was searched for "Artificial intelligence" and "COVID-19" and "respiratory care unit" written in the English language during a period of one year 2019-2020. The relevance of AI for patients is in hands of people with digital health tools, Aarogya setu app and Smartphone technology. AI shows about 95% accuracy in detecting COVID-19-specific chest findings. Robots with AI are being used for patient assessment and drug delivery to patients to avoid the spread of infection. The pandemic outbreak has replaced the classroom method of teaching with the online execution of teaching practices and simulators. AI algorithms have been used to develop major organ tissue characterization and intelligent pain management techniques for patients. The Blue-dot AI-based algorithm helps in providing early warning signs. The AI model automatically identifies a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection sound pressure, and light level detection. There is now no looking back as AI and machine learning are to stay in the field of training, teaching, patient care, and research in the future.

Multi-Omics Approach in the Identification of Potential Therapeutic Biomolecule for COVID-19

COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has a disastrous effect on mankind due to the contagious and rapid nature of its spread. Although vaccines for SARS-CoV-2 have been successfully developed, the proven, effective, and specific therapeutic molecules are yet to be identified for the treatment. The repurposing of existing drugs and recognition of new medicines are continuously in progress. Efforts are being made to single out plant-based novel therapeutic compounds. As a result, some of these biomolecules are in their testing phase. During these efforts, the whole-genome sequencing of SARS-CoV-2 has given the direction to explore the omics systems and approaches to overcome this unprecedented health challenge globally. Genome, proteome, and metagenome sequence analyses have helped identify virus nature, thereby assisting in understanding the molecular mechanism, structural understanding, and disease propagation. The multi-omics approaches offer various tools and strategies for identifying potential therapeutic biomolecules for COVID-19 and exploring the plants producing biomolecules that can be used as biopharmaceutical products. This review explores the available multi-omics approaches and their scope to investigate the therapeutic promises of plant-based biomolecules in treating SARS-CoV-2 infection.

The global experience of digital health interventions in COVID-19 management

Digital health interventions are globally playing a significant role to combat coronavirus disease 2019 (COVID-19), which is an infectious disease caused by Severe Acute Respiratory Syndrome coronavirus 2. Here, we present a very brief overview of the multifaceted digital interventions, globally, and in India, for maintaining health and health-care delivery, in the context of the Covid-19 pandemic.

Addressing COVID-19 Drug Development with Artificial Intelligence

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that led to the COVID-19 (Coronavirus Disease 2019) pandemic, has resulted in substantial overburdening of healthcare systems as well as an economic crisis on a global scale. This has in turn resulted in widespread efforts to identify suitable therapies to address this aggressive pathogen. Therapeutic antibody and vaccine development are being actively explored, and a phase I clinical trial of mRNA-1273 which is developed in collaboration between the National Institute of Allergy and Infectious Diseases and Moderna, Inc. is currently underway. Timelines for the broad deployment of a vaccine and antibody therapies have been estimated to be 12-18 months or longer. These are promising approaches that may lead to sustained efficacy in treating COVID-19. However, its emergence has also led to a large number of clinical trials evaluating drug combinations composed of repurposed therapies. As study results of these combinations continue to be evaluated, there is a need to move beyond traditional drug screening and repurposing by harnessing artificial intelligence (AI) to optimize combination therapy design. This may lead to the rapid identification of regimens that mediate unexpected and markedly enhanced treatment outcomes.