Novel Therapeutic Targets for SARS-CoV-2 and COVID-19

Symptoms

Symptoms of COVID-19 are many ^, ; some of them are as mild as those of normal influenza and others quite severe. Treatment provided to a patient with mild COVID-19 symptoms may be different from given to one with more serious symptoms such as difficulty of breathing. COVID-19 may lead to a few other manifestations such as cardiac problems in criti­cally ill patients.

Most patients experience mild to moderate respiratory illness among other symptoms and recover quickly without requiring any special treatment. Aged patients and those with prior medical issues such as cardiovascular disease, diabetes, chronic respiratory disease, and cancer are at risk for developing serious complications and other manifestations. Several chapters in this book deal with varied manifestations, indicating that COVID-19 treatment is about not only respiratory illness but also potentially other severe symptoms, including tissue damage.

Disease Progression and Severity

In the time course of COVID-19, there are three stages: (1) early infection phase, (2) pulmonary phase, and (3) hyperinflammation phase. ^, It is important to note that some patients have only mild symptoms associated with an upper respiratory tract infection, or stage 1, whereas others progress to more advanced stages. The medication varies across stages. Pneumonia sets in at the pulmonary phase. Stage 3, or the hyperinflammation phase, leads to organ failures. Avoiding a cytokine storm remains a critical challenge in the progression of the disease.

Prevention and Control of Infection

To slow transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) one needs to know about the virus, the disease itself, and how it could spread. Social distancing and washing hands or using an alcohol-based rub frequently go a long way to avoid spread of the virus. SARS-CoV-2 spreads primarily through droplets of saliva or discharge from the nose when an infected person coughs or sneezes, which is why wearing face masks and social distancing have been shown to be effective against the spread of the virus. The transmission of SARS-CoV-2 also can be airborne.

In addition to the previously mentioned basic protective measures, when infection is suspected, identification of certain biomarkers becomes necessary for COVID-19 treatment. To form a therapeutic strategy, it is important to classify severity based on identified immune-related biomarkers so that appropriate treatment would be provided. A list of such biomarkers includes the following types:

• 1.

  Hematological (lymphocyte count, neutrophil count, neutrophil-to-lymphocyte ratio [NLR])

• 2.

  C-reactive protein (CRP)

• 3.

  Erythrocyte sedimentation rate (ESR)

• 4.

  Procalcitonin (PCT)

• 5.

  Interleukin-6 (IL-6)

• 6.

  Biochemical ( d -dimer, troponin, creatine kinase [CK], aspartate aminotransferase [AST]), especially those related to coagulation cascades in disseminated intravascular coagulation (DIC) and acute respiratory distress syndrome (ARDS)

Experimental Studies to Identify Druggable Targets

A target is called “druggable” if its activity (or function) can be modulated by a therapeutic drug. Proteins and nucleic acids are both examples of biological targets. A potential drug target should have druggability, have an acceptable toxicity profile, and be assayable in addition to the fact that its three-dimensional (3D) structure is known. There are two classes of methods for identification of targets, target deconvolution and target discovery. A gamut of experimental methods to identify targets are known both in vitro and in vivo and described in a seminal reference. The experimental methods include various affinity methods and genetic methods. Affinity chromatography is one of the most used methods for target deconvolution. Target deconvolution means looking for a target for an active compound. This results in screening of many molecules. In the target discovery method, the search is for a new target for which there is not yet a drug compound. Various biological assays and bioinformatics approaches are used for target discovery and target-based drug discovery.

Biological Assays

To understand the molecular mechanisms of disease, scientists perform experiments, specifically assays. In vitro assays are cell-based models, and in vivo assays are carried out inside living systems. Many different assays are used for drug target identification and validation; a few of them are mentioned here as examples. One major class of assays used in target identification and validation is functional assays, which are experiments designed to determine the role or function of a protein in a biological process or pathway.

As an example, to determine the activity of the target RNA-dependent RNA polymerase (RdRp) (NSP12), typical assays include the following ¹⁴ :

• 1.

  Polymerase elongation template element (PETE)

• 2.

  Fluorescence-based alkaline phosphatase–coupled polymerase assay (FAPA)

• 3.

  Fluorometric RdRp activity assay

• 4.

  Scintillation proximity assay

• 5.

  Cell-based assays

When these assays are carried out in the presence of an antagonist, a decrease in the replication activity may be observed. To find the efficacy of the drug remdesivir, which has been approved by FDA for treatment of COVID-19, such assays were used to determine its inhibitory activity against RdRp.

Bioinformatics Approaches

Traditionally, in bioinformatics approaches, algorithms such as BLAST are used, to compute similarity of a given protein against proteins with a known structure and function present in bioinformatics databases. Through this process, one can predict the structure and function of a given protein by comparing its similarity with known targets. In every drug discovery project, one is also able to leverage and capitalize on the data available from experiments carried out previously, aided by bioinformatics, which is being further enhanced by advanced data-driven techniques. In the case of SARS-CoV-2, much of the knowledge from other virus systems such as SARS-CoV, Ebola, and Middle East respiratory syndrome coronavirus (MERS-CoV) led to recognition of similar target types for SARS-CoV-2.

Bioinformatics not only provides historical experimental data on structure, activity, and other properties of biomolecules but also gives insights from analysis of the data. Further, it provides many approaches not only for identification of targets but also for drug discovery. Current trends in bioinformatics encompass an approach that integrates many factors such as predicting disease-relevant genes, constructing gene networks, protein–protein interaction networks, biological pathway analysis, etc.

In silico or computational techniques, including molecular modeling techniques, can provide information on a variety of factors, such as:

• 1.

  Binding orientation of small molecule inhibitors with protein structures in 3D space through molecular docking techniques

• 2.

  Information on functional groups such as amino acid residues and other moieties that bind to the drug

• 3.

  Stability and properties of ligand–protein complexes using molecular dynamics simulation

A combination of these techniques as part of an integrated approach guide the researcher in discovering new drug molecules and new drug targets. A summary of methods of target and drug discovery is given in Box 17.1 .

The literature published in the last 18 months reveals the extensive use of various bioinformatics approaches related to COVID-19. ^, Bioinformatics enables scientists to reduce the number of experiments and quicken the target discovery, drug discovery, or drug repurposing process. Drug repurposing is particularly attractive because the knowledge of the drug safety is already available, which saves time and reduces cost.

In addition to the previous description, it is important to point out that bioinformatics approaches are enhanced by integration of various data. Genomics, transcriptomic, and metabolomic data are integrated with proteomics (proteins) data through data science techniques to make predictions about protein biomarkers of a disease. The predictive modeling method helps narrow the search space of druggable protein targets from a list of proteins involved in the disease pathway. The druggable proteins identified should be unique to the disease to avoid off-target effects.

To identify the differential expression of genes and their protein products in the diseased and the normal case, various forms of high-throughput expression profiling is carried out and data are deposited in many public research databases, such as the National Center for Biotechnology Information Expression Atlas. ^, Various forms of data analysis–based machine learning algorithms are used to identify the differentially expressed genes, which are then mapped to their respective protein products. The protein products that are identified with druggable pockets and unique differential expression with respect to the disease are proposed as possible therapeutic targets.

Further, functional assays are carried out to validate a proposed target. If target protein inhibition is proposed as a therapeutic strategy to overcome the overexpression of the target protein in the disease, gene knockout experiments are performed, in which the gene corresponding to the target is knocked out and thus the target protein is not expressed; if an attempt to induce the disease fails in such a case, the target can be considered and experimentally validated. Although experimental methods are still required to validate proposed targets, the machine learning and bioinformatics approaches are useful to carry out the data analysis required to identify perturbed molecular pathways in a disease, which leads to identifying novel therapeutic targets. A dedicated resource of COVID-19–related differential gene expression data is available at CovidExpress for the leveraging of bioinformatics and machine learning approaches in identifying novel therapeutic targets.

A recent example of identifying a drug target and repurposing a known drug molecule for COVID-19 is related to baricitinib, a known immunomodulator. Baricitinib is an oral Janus kinase (JAK)1/JAK2 inhibitor approved for the treatment of rheumatoid arthritis. Based on a large repository of structured medical information extracted from the scientific literature using machine learning, scientists identified JAK1/JAK2 and AP2-associated protein kinase 1 (AAK1) proteins as potential targets for treatment of COVID-19. ^, AAK1 is known to regulate endocytosis. Further, it was predicted through modeling studies that baricitinib would bind to the AAK1 protein as well, which would inhibit virus entry. A combination of baricitinib with an antiviral drug (remdesivir) is in clinical testing. Thus a multifaceted approach comprising experimental, theoretical, computational, and data science–based techniques is used to identify drug targets and drug molecules for COVID-19.

The host genetic diversity that determines susceptibility to SARS-CoV-2 and disease severity makes a case for the use of precision medicine methodology in COVID-19 therapeutics. One of the challenging problems in COVID-19 treatment, for example, is the knowledge gap of predicting which patients have a genetic predilection for a cytokine storm and thus would benefit most from immunosuppressive medication such as corticosteroids. To deal with this challenge, Lam C. et al. used a precision medicine–based artificial intelligence (AI) ^a

a AI stands for artificial intelligence approaches, in which by the use of trained mathematical models, structure and properties are predicted.

model to make recommendations on the use of corticosteroids for COVID-19.