# Bayesian Model Infers Drug Repurposing Candidates for Treatment of COVID-19
## 4 Discussion  
The COVID-19 pandemic has ignited a sense of urgency, purpose, and scientific/medical necessity to engage the entire armamentarium of technology, informatic capabilities, therapeutic tools and collaborative spirit to combat this accelerating foe of humanity. Due to the rapid growth kinetics of the disease and impact globally, utilizing emerging crowd sourced data and applying innovative bioinformatic modeling is an optimal approach to illuminate \begin{table}
\begin{tabular}{c c c} \hline \hline
**Drug Signature** & **Fold Enrichment** & _p-Value_ \\ \hline emetine HL60 DOWN & 5.14 & 2.46 E-07 \\ \hline ambroxol PC3 DOWN & 3.99 & 1.30 E-06 \\ \hline Vitinoin CTD 00007069 & 3.36 & 3.85 E-06 \\ \hline diclofenac CTD 00005804 & 4.09 & 1.91 E-05 \\ \hline sertraline CTD 00007358 & 8.42 & 2.03 E-05 \\ \hline ellagic acid BOSS & 5.84 & 7.23 E-05 \\ \hline ellagic acid CTD 00005891 & 6.36 & 0.00012 \\ \hline ascorbic acid CTD 00005445 & 4.73 & 0.00013 \\ \hline piperine TTD 00010245 & 21.65 & 0.00034 \\ \hline oxygen CTD 00006454 & 3.25 & 0.00035 \\ \hline Fulvestrant CTD 00002740 & 3.91 & 0.00053 \\ \hline carbamazepine CTD 00005574 & 3.03 & 0.00183 \\ \hline menadione PC3 DOWN & 4.64 & 0.00193 \\ \hline indomethacin CTD 00006147 & 3.27 & 0.00328 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of drug signature based on enrichment analysis to the titer. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target** & **Drug Name** & **Indications** & **Development Status** \\ \hline \multirow{6}{*}{MAOB} & rasagiline & Parkinsons disease & Launched \\  & ropinirole & Parkinsons disease; & Launched \\  & safinamide & Movement disorder; Muscle disease; & Launched \\  & selegiline & Parkinsons disease & Launched \\  & tolcapone & Parkinsons disease & Launched \\ \hline \multirow{6}{*}{CHECK1} & \multirow{6}{*}{SRA-737} & Cancer; Colorectal tumor; & \\  & & Non-small-cell lung cancer; Ovary & \\ \cline{1-1}  & & tumor; Small-cell lung cancer; Soft & Phase 2 Clinical \\ \cline{1-1}  & & tissue sarcoma; Transitional cell & \\ \cline{1-1}  & & carcinoma; Uterine cervix tumor & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of targets with approved drugs or in advanced clinical development. potential drug-targets to mitigate the SARS-CoV-2 infection [10]. Thus, we engaged a Bayesian network approach to identify potential therapeutic targets [6; 9] causally integrated with viral titer. Multiple informatic approaches have been applied to the integration of population level, molecular and imaging data associated with SARS-CoV-2 infection. These include conventional interactome networks, associations with abundance trajectories with viral proteins, neural networks, deep and self-supervised learning, as well as machine learning of digital data [8; 10; 16; 17; 18; 19; 20]. Each type of informatic approach has its advantages and disadvantages based on a priori knowledge, power and structure of the dataset, as well as the ability to develop de novo knowledge. Due to the novel nature of SARS-CoV-2, we approached this study with a data agnostic approach based on Bayesian causality to derive unique relationships of molecules in a data driven manner. Despite the limited data size, which was underpowered to support the inferred causalities, the robustness of bAlcis(r) analysis was evidenced by the data-driven linkage of SARS-CoV-2 titer to ACE2 [21]. Several studies have confirmed that ACE2 serves as a receptor for binding of the SARS-CoV-2 spike proteins required for host cell entry and viral replication [2]. Overexpression of ACE2 from different species, including human (HeLa cells), pig and civet, was associated with SARS-CoV-2 infection and replication, further demonstrating its role as a receptor for entry into host cells [22]. Furthermore, SARS-CoV-2 entry into host cells is dependent on ACE2 and serine protease TMPRSS 2 for priming, an activity that can be blocked by a specific inhibitor [23].