Previously, we have evaluated the DeepNEU (v5.0) machine learning platform for simulating uninfected (aiLUNG) and SARS-CoV-2 infected differentiated Type 1 (AT1) and Type 2 (AT2) alveolar lung cells (aiLUNG-COVID-19).The primary purpose of this project is to extend our previous research into COVID-19 drug discovery, and to enable the rapid identification of therapeutic targets and drug repurposing specifically for treating both the cytokine storm and coagulopathy frequently seen in patients with severe COVID-19.
In our recent publication (Esmail and Danter, 2020. Accepted)*, we presented data confirming that the DeepNEU (v5.0) platform can accurately simulate AT1 and AT2 lung cells based on the direct generation of human lung cells (iLUNG) from iPSC that is well documented in the peer reviewed literature (39,40). Importantly, these aiLUNG cells exhibited the same genotypic and phenotypic markers as iLUNG cells that were generated in wet lab experiments (39-41). In addition, when the aiLUNG cells were infected with simulated SARS-CoV-2 the phenotypic (N=8) and genotypic (N=17) features of aiLUNG-COVID-19 were all in agreement with the current literature (2,29,32,39-51). When we combined these genotypic and phenotypic profiles, the probability that all (N=25) features of simulated aiLUNG-COVID-19 were accurately predicted by chance alone using the binomial test is 0.00003. To summarize, the data from our previous experiments indicated that SARS-CoV-2 viral infection (COVID-19) could be accurately simulated in aiLUNG cells using the DeepNEU (v5.0) machine learning platform (15).
Once the aiLUNG and aiLUNG-COVID-19 simulations were validated against the peer reviewed wet lab research, we applied them to therapeutic target identification and drug re-purposing. In the case of severe COVID-19, no re-purposed therapies are currently approved. The rational evaluation of currently licensed drugs to identify potentially effective therapies or simple double drug combinations may represent the most efficient path to improved patient outcomes when combined with respiratory support plus early and widespread testing.
The use of stem cells (iPSC) for targeted drug discovery has been well reported in the peer reviewed literature (52-54) and our approach to using the aiPSC derived aiLUNG-COVID-19 and aiLUNG simulations for COVID-19 specific drug repurposing has been previously described in detail (15). In the current paper the same approach was used but in this project our drug repurposing efforts were focused specifically on mitigating the cytokine storm and coagulopathy often seen with life threatening COVID-19. As of this writing the only widely available therapies showing promise are the early administration of systemic corticosteroids like dexamethasone and anticoagulation with heparin (55-57)
Evaluating the aiLUNG-COVID-19 simulations for repurposing double drug combinations to mitigate COVID-19 associated cytokine storm and coagulopathy
We had previously compared all genotypic features for >4100 factors from the aiLUNG-COVID-19 and aiLUNG simulations using the two tailed Mann–Whitney u test. A subset of seventeen factors emerged as potential therapeutic targets. This subset of highly significant (p=0.00001) genotypic factors (N=17) were selected for initial evaluation as previously outlined in(15). Inhibiting each of the seventeen factors in an iterative process produced variable effects on COVID-19 associated cytokine storm. As outlined above, the rational for including Hydroxychloroquine (HCQ) in the initial screening run was also proposed and interestingly HCQ was the only agent capable of significantly mitigating the cytokine storm during the initial screening. These significant results indicate that the COVID-19 associated cytokine storm and coagulopathy are more likely to be dependent on the host response to infection than on the SARS-CoV-2 genome itself. Therefore, anti-viral therapy primarily targeting the SARS-CoV-2 genome should not be expected to mitigate the cytokine storm and coagulopathy. In established and progressing COVID-19 other targeted approaches will be required (57-60).
To address this probable reality, we carried out a comprehensive review of the recent therapeutic literature to supplement our analysis and identified ten additional therapeutic candidates (11,19,25-38). These new candidates were confirmed by a more detailed statistical analysis of the aiLUNG vs aiLUNG-COVID-19 data and include Cathepsins B and L, COX2, IL-1ab, IL-6, Interferon ab, JAK1-3 and TNFa as summarized above in Table 4. This new data allowed us to evaluate a more extensive group of single drug candidates (N=16) in a second screening run. In the cytokine storm aiLUNG-COVID-19 simulations the most effective single agent from the second screening run was an inhibitor of IL-1ab followed by inhibitors of IL-6, TNFa, COX2, IFN1ab and the addition of HCQ, when the outputs from aiLUNG (uninfected) cells and untreated aiLUNG-COVID-19 simulations were ranked using the ACD metric (Figure 2A). Similarly, the experiments applying the drug candidates to the coagulopathy simulations also identified inhibitors of IL-1ab, IL-6, TNFa, COX2, IFN1ab and the addition of HCQ (Figure 2B). These encouraging results allowed us to proceed with the evaluation of double drug combinations.
Based on these findings a final screening round was carried out to evaluate fifteen double drug combinations. The individual double drug combinations evaluated are presented in detail in Table 2 above. From the cytokine storm simulations, thirteen of the double drug combinations evaluated were effective on the fourteen viral targets profile outlined above. Ranking the most effective combinations according to the Angular Cosine Distance (ACD) from the uninfected profile indicate that overall, the most effective double drug combination for mitigating the cytokine storm is HCQ plus an inhibitor of TNFa as presented in Figure 3A.
The analysis of the coagulopathy simulations also revealed that thirteen of the double drug combinations were effective based on the seventeen viral targets profile outlined above. A final ranking of these effective combinations based on the ACD metric indicated that overall, the most effective double drug combination for ameliorating the Coagulopathy appears to be a COX2 inhibitor plus an inhibitor of IL-1ab as presented in Figure 3B.
A central purpose of this project was to determine if any double drug combinations could mitigate both the cytokine storm and the coagulopathy associated with severe COVID-19. To that end we ranked the effective combinations based on their ACD metric and determined that seven of the double drug combinations ranked higher than the best of the single drugs based on their potential to alleviate both the cytokine storm and the coagulopathy complications. The final ranking based on the average ACD metric indicated that overall, the most effective double drug combination was a COX2 inhibitor plus an inhibitor of IL-1ab followed closely by a combination of either a COX2 inhibitor plus an inhibitor of IL-6 or HCQ plus an inhibitor of TNFa (Figure 4).
Importantly, all of these effective double drug combinations can be clinically evaluated because the approved drugs are currently available for urgent clinical study. For example, a single specific COX2 inhibitor, namely Celecoxib, is already widely available by prescription in North America. Similarly, HCQ is also widely available for use in North America. Although the results from clinical trials exploring the effectiveness of HCQ in preventing or treating COVID-19 have so far been disappointing, combining HCQ with a TNFa inhibitor has not yet been evaluated. Monoclonal antibody inhibitors of TNF including infliximab, adalimumab, certolizumab pegol and golimumab are available and are widely prescribed. Etanercept, a circulating fusion protein TNF receptor inhibitor, is also available. A number of small molecules including thalidomide, lenalidomide and pomalidomide are also active as TNF inhibitors. Anakinra is a recombinant modified version of the human IL-1 receptor antagonist protein that has proven effective in life threatening sepsis and is available for clinical study in severe COVID-19 (31). Available monoclonal antibody inhibitors of IL-6 include tocilizumab and sarilumab, which are IL-6 receptor inhibitors and siltuximab which targets IL-6 itself.
Comparing the efficacy of single and double drug combinations in cytokine storm and coagulopathy
Finally, we compared the effectiveness of single drugs and double drug combinations in complicated COVID-19. Our analysis indicated that (1) for the cytokine storm simulations the double drug combinations (N = 13, average ACD ± 95%CI = 0.046 ± 0.019) were generally more effective than single drugs (N = 7, 0.022 ± 0.005, two tailed Mann-Whitney u test p <0.01) and (2) similarly for the coagulopathy simulations the double drug combinations (N=13, average ACD ± 95%CI = 0.103 ± 0.008) were also generally more effective than single drugs (N=7, 0.167 ± 0.041, two tailed Mann-Whitney u test p <0.01).
These results support the conclusion that for serious diseases, drug combinations are generally more effective than single drugs (61,62). With a few exceptions like Gleevec for Chronic Myelogenous Leukemia (CML), this is particularly true in the majority of cancers, in life threatening sepsis and in situations where immune suppression is essential.
We remain aware that predictions from advanced computer simulations still require wet lab confirmation and this continues to be important for DeepNEU v5.0 as well. However, a key aim of this project was to make these findings regarding the potential therapeutic benefit of novel double drug combinations for mitigating the complications of serious COVID-19 freely available to the global research community for wet lab validation at the very earliest opportunity. We also plan to validate these important findings and we are currently looking for development partners with the goal of confirming them in animal models of severe COVID-19. We commit to making any additional information available at the earliest opportunity.