Simulating target site concentration for COVID-19 drugs
Figure 4 shows the simulated relative exposure of lung to plasma at steady state, depicting unbound epithelial lining fluid concentrations along with 95% prediction intervals. The horizontal dashed lines show IC50 and IC90 (9 x IC50) range of target inhibition. The input parameters used for the permeability-limited lung model [14] are shown inSupplementary Table 1 and Supplementary Table 2.Overall, all COVID-19 drugs appear to reach adequate exposures at steady-state over the target IC50 values of respective drugs and stays above for minimum of 8hr for all drugs except for all anti-viral drugs where reported IC50 values against SARS-CoV-2 were reported [27]. Nevertheless, these anti-virals exposure were above IC50/IC90 if human immunodeficiency virus (HIV) target values were used. Achieving 90% of target inhibition with unbound ELF concentration in most of the patients is achievable for a period of time (>4 hrs; Figure 4 ) except for all anti-viral drugs. Total (parent + metabolite) unbound plasma and ELF concentration were used to estimate an average BTK occupancy for acalabrutinib (a covalent BTK binder). Estimated BTK occupancy was >95%, is consistent with the observed effectiveness of acalabrutinib in COVID-19 patients (NCT04346199) [3].
It has been proposed that the virus is internalized by receptor mediated endocytosis and delivered to lysosomes where it replicates. Some of these drugs have shown efficacy in raising the lysosomal PH (lysosomotropic drugs), resulting in lysosomal trapping of the virus and preventing its spread within the cell. This in theory might help the drug to be concentrated at the target site and thus, lower doses of the drug could to be required to achieve therapeutic efficacy. The lysosomotropic potential for COVID-19 drugs were predicted based on Ufuk et al., [28] and shown in Supplementary Table 2 . This lysosomotropic potential of chloroquine, hydroxychloroquine, atazanavir and remdesivir were appeared to be beneficial for attaining a required target exposure and efficacy. However increased exposure might also have consequences towards side effects.

Sensitivity analyses with varying degrees of CYP suppression by cytokine storm

The relative change in exposure (AUC) for CYP3A substrates such as acalabrutinib and ibrutinib when CYP3A expression reduced to one tenth of its healthy level in both the gut and liver is shown inFigure 5 . As a worst-case scenario, reducing 90% of CYP3A4 abundance in liver and gut, resulted in ~4-fold increase in AUC for acalabrutinib, while it was 15-fold change for ibrutinib.
Discussion
Whilst efforts for developing vaccines against COVOD-19 is intensively continuing, there are no indications that any of the programs will provide a safe and effective vaccine at the scales needed to reverse the pandemic caused by the virus before the end of 2020. Public health measures have been successful to various degrees in different countries in reducing the rate of infection. However, as these measures ease off to balance the negative economic prospects of lockdowns and social distancing, many expect that spread of the virus will continue albeit at lower rate even if we avoid a second peak. Therefore, ability for therapeutic management of patients who are infected remains a viable and necessary element of fight against the pandemic in parallel to the development of vaccines and public health measures to control the spread.
Great advances have been made in the use of modeling tools to predict and optimize doses and dosing schedules for clinical trial optimization and to inform drug labels [13, 29-31]. The current situation surrounding COVID-19 requires fast decision in clinical trial design with limited information, consequently, resulting in a potentially higher risk or lower benefit. A few such examples include higher incidence of side effects for chloroquine and lack of benefit from lopinavir and ritonavir combination therapy [32, 33]. COVID patients who received chloroquine with azithromycin experienced QTc prolongation, which may be attributed to PK or PD changes in target tissues as a result of DDI. To help drug developers and regulators surrounding the development of candidate treatments and regimens for Covid-19, reliable and effective modelling tools for quantifying and comparing therapy options, particularly in cases where clinical data are scant, will be very useful.
For typical drug development, in theory, the effect of all extrinsic and intrinsic patient factors can be tested clinically. However, ethical and practical issues limit the numbers of feasible studies one can conduct. As shown in Supplementary Figure 1 , there are more than 100 untested clinical scenarios for COVID-19 repurposed drugs, due to limited time and resources. In this current COVID-19 pandemic, PKPD and PBPK modeling tools are already helping to optimize and accelerate candidate therapeutics [26, 32].
PBPK modeling approaches integrate drug data and physiological or system data together with patient factors, to predict certain untested situations using current knowledge. In recent years there have been several examples where PBPK modelling has been used in lieu of many clinical studies [30]. Currently, there is a lack of knowledge regarding intrinsic and extrinsic factors on the disposition of repurposed drugs for COVID-19. Therefore, in this work, we a) present a summary of ADME, PK/PD, DDI; b) provide a repository of PBPK models, input parameters to simulate the unbound plasma and/or lung epithelial lining fluid concentrations; and c) utilize PBPK models to predict expected alterations in exposure to these drugs in sub-populations of COVID-19 patients which have not yet been studied for some of the drugs in patients with older age, different race, and hepatic/renal impairment. The drugs selected for this analysis relied on verified PBPK model availability, and robust in vitro and clinical evidence of their effects on drug clearance.
Creating innovative solutions for dosing optimization under time constraints comes with uncertainty and gaps in knowledge which can only be addressed as more data becomes available. We can, however, prioritize available resources to reduce the number of patients-at-risk due to sub-optimal dosing and dosing-schedules, by simulating the situations where no clinical data are available. In this way, we can establish a scientific fact-base and triaging process, to accelerate our understanding of therapeutic interventions for COVID-19. To facilitate knowledge‐sharing and advance the field, modelling workspaces can be uploaded to an open-access, modelling tool platform members’ area or Github (http://github.com/). Supplementary Files (workspaces) andSupplementary Table 1 provide PBPK models that can be readily accessed by scientists for use in their own research.
Table 1, 2, and 3 provide PK, PD, DDI and clinical pharmacology information for COVID-19 drugs. No clinical DDI data are available in many instances for example, combination of ritonavir (a CYP3A reversible and Time dependent inhibitor) with BTK inhibitors such as ibrutinib and acalabrutinib. Based on PBPK modeling -the DDI risk appears to be higher with ibrutinib when combined with ritonavir (AUC ratio of ~44-fold), while ritonavir combination increased acalabrutinib AUC by 5.7-fold); however, no DDI risk is anticipated for dapagliflozin, as it is mainly eliminated by UGT1A9 substrate.
As many of the repurposed drugs studied in this paper are primarily metabolized in the liver, it was unsurprising to find that impaired liver function or liver disease impacts the PK of these drugs. Baricitinib is mainly excreted unchanged by the kidney and thus its PK can be altered in renal failure conditions. Figure 3 andTable 3 and 4 shows the simulations for COVID-19 drugs under organ impairment situations. PBPK models suggests dosing adjustment for CYP3A substrates like ibrutinib, dexamethasone, and acalabrutinib likely to be necessary.
Sensitivity analyses can be a useful tool, to study the uncertainty in model input parameter and assess its impact on simulated PK profiles. For instance, in rheumatoid arthritis and cancer, it was reported that CYP3A4 content in the liver and gut was reduced by ~30% [12] but such meta-analyses and reports are not yet available for COVID-19. Automated sensitivity analysis using Ibrutinib and acalabrutinib as a tool compounds suggest around 15-fold and ~4-fold increase in AUC, if the CYP3A levels are reduced by 90% from normal levels (Figure 5 ), respectively.
PBPK enables better handling of tissue site concentrations compared to population-based PK modelling, and we can account for disease-drug interaction i.e. cytokine storm. In this study we utilize in vitro IC50 rather than in vivoEC50 due to scarcity of this data. Nevertheless, when unbound in vivo EC50 data becomes available, we can easily update the exposure profiles with relevant target inhibition values and explore IVIVE as shown in Pilla Reddy et al., [34]
In vitro IC50 were generated for anti-viral drugs using serum free media in most instances, and thus we can assumein vitro IC50 are basically unbound values; however, in-vivo lung tissue and plasma profiles should be corrected for lung tissue protein binding and plasma protein binding respectively to obtain KP,uu, under the assumption that the unbound drug is pharmacologically active [26].
Simulating the plasma and/or lung exposure of drugs used in COVID-19 therapies is of importance, as the unbound tissue concentration drives the efficacy and safety of a drug. Non-invasive imaging methods (PET/MRI) can be used to determine tissue drug concentrations; however, it is logistically challenging to routinely employ these methods during drug development. We therefore need high-throughput and cost-efficient methods to predict the tissue concentration of drugs. In order to do so, we must accurately predict drug distribution and clearance into and out of tissue. We hypothesize that lung concentrations of drugs can be predicted through in vitro to in vivo extrapolation, by incorporating the relevant parameters such as permeability and transporter data. Preclinical, quantitative, whole-body autoradiography distribution data could be used to understand the partition between plasma and lung tissue or organ of interest to some extent, assuming the unbound partition coefficient is independent of species. Quantitative whole-body autoradiography data in rats was available for most of the drugs (Supplementary Table 2 ) allowing comparisons with predicted total KP values within the Simcyp Simulator.
The permeability-limited lung model within the Simcyp Simulator has been used and verified with known anti-tuberculosis drug concentrations in epithelial lining fluid (ELF). This prediction of ELF unbound concentration is particularly relevant for COVID-19 patients, as distribution of COVID-19 drugs should be targeted into immune privileged sites like ELF in the lung, which may represent a persistent reservoir for the virus [11]. Highly ionizable drugs might experience different lung uptake due to changes in lung pH due to COVID-19, which were accounted for in our PBPK model. As shown in Figure 4 , simulated unbound ELF profiles were compared with in vitropotency values that are thought to inhibit SARS-CoV-2, and most drugs exhibited reasonable duration of target inhibition.
In summary, in this study, we provide a database for relevant PK, PD, DDI and AEs attributes for ongoing COVID-19 treatment, including both small molecules and large molecules. Furthermore, we have highlighted the application of quantitative modelling tools that could be helpful to understand the intrinsic and extrinsic factors that can affect the PK of these repurposed small molecule drugs, including the strategy of simulating the plasma and/or ELF concentrations under physiological changes of COVID-19 population. Prospective PBPK modelling and simulations can identify gaps in available datasets and play a role in answering critical clinical questions related to patient intrinsic and extrinsic factors that can’t be studied in the time-constrained COVID-19 pandemic.
Acknowledgments
The authors would like to thank the patients, their families, and the clinical teams who worked on the studies.
Conflicts of Interest
Venkatesh Pilla Reddy, Heeseung Jo, Natalie Giovino, Emily Lythgoe, Shringi Sharma and Weifeng Tang, are all employed by for AstraZeneca. Eman Elkhateeb is a Ph.D. student at University of Manchester. Prof Amin Rastomi and Masoud Jamei holds shares in Certara, a company focusing on Model‐Informed Drug Development.
Funding
The study was supported by AstraZeneca. Eman is supported by PhD funding from the Egyptian government and CAPKR
Author contributions
All authors were involved in designing the studies and performing the study analyses. All authors wrote the manuscript. VPR, EE, and HJ performed the modelling analyses. All authors have approved the manuscript for submission.
Data accessibility statement
The authors confirm that the clinical data supporting the PBPK modeling are listed in clinicaltrial.gov and details of these clinical studies published elsewhere. The data that support the findings of this study are available from supplementary files and repository of PBPK files can be found in Simcyp members area
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