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
References
1. FDA. Enhancing the Diversity of Clinical Trial Populations —
Eligibility Criteria, Enrollment Practices, and Trial Designs Guidance
for Industry. 2019.
2. Beigel JH, Tomashek KM, Dodd LE, Mehta AK, Zingman BS, Kalil AC,
Hohmann E, Chu HY, Luetkemeyer A, Kline S, Lopez de Castilla D, Finberg
RW, Dierberg K, Tapson V, Hsieh L, Patterson TF, Paredes R, Sweeney DA,
Short WR, Touloumi G, Lye DC, Ohmagari N, Oh MD, Ruiz-Palacios GM,
Benfield T, Fatkenheuer G, Kortepeter MG, Atmar RL, Creech CB, Lundgren
J, Babiker AG, Pett S, Neaton JD, Burgess TH, Bonnett T, Green M,
Makowski M, Osinusi A, Nayak S, Lane HC, Members A-SG. Remdesivir for
the Treatment of Covid-19 - Preliminary Report. N Engl J Med 2020.
3. Roschewski M, Lionakis MS, Sharman JP, Roswarski J, Goy A, Monticelli
MA, Roshon M, Wrzesinski SH, Desai JV, Zarakas MA, Collen J, Rose K,
Hamdy A, Izumi R, Wright GW, Chung KK, Baselga J, Staudt LM, Wilson WH.
Inhibition of Bruton tyrosine kinase in patients with severe COVID-19.
Sci Immunol 2020; 5.
4. Treon SP, Castillo JJ, Skarbnik AP, Soumerai JD, Ghobrial IM,
Guerrera ML, Meid K, Yang G. The BTK inhibitor ibrutinib may protect
against pulmonary injury in COVID-19-infected patients. Blood 2020; 135:
1912-15.
5. Administration FFD. FDA Guidance on Conduct of Clinical Trials of
Medical Products during COVID-19 Public Health Emergency. 2020.
6. Jarvis CI, Van Zandvoort K, Gimma A, Prem K, group CC-w, Klepac P,
Rubin GJ, Edmunds WJ. Quantifying the impact of physical distance
measures on the transmission of COVID-19 in the UK. BMC Med 2020; 18:
124.
7. Gautret P, Lagier JC, Parola P, Hoang VT, Meddeb L, Mailhe M, Doudier
B, Courjon J, Giordanengo V, Vieira VE, Dupont HT, Honore S, Colson P,
Chabriere E, La Scola B, Rolain JM, Brouqui P, Raoult D.
Hydroxychloroquine and azithromycin as a treatment of COVID-19: results
of an open-label non-randomized clinical trial. Int J Antimicrob Agents
2020: 105949.
8. Aziz M, Fatima R, Assaly R. Elevated Interleukin-6 and Severe
COVID-19: A Meta-Analysis. J Med Virol 2020.
9. Shimabukuro-Vornhagen A, Gödel P, Subklewe M, Stemmler HJ, Schlößer
HA, Schlaak M, Kochanek M, Böll B, von Bergwelt-Baildon MSJJfioc.
Cytokine release syndrome. 2018; 6: 56.
10. Singhal T. A Review of Coronavirus Disease-2019 (COVID-19). Indian J
Pediatr 2020; 87: 281-86.
11. Ye Q, Wang B, Mao JJTJoi. The pathogenesis and treatment of
theCytokine Storm’in COVID-19. 2020.
12. Schwenger E, Reddy VP, Moorthy G, Sharma P, Tomkinson H, Masson E,
Vishwanathan K. Harnessing Meta-analysis to Refine an Oncology Patient
Population for Physiology-Based Pharmacokinetic Modeling of Drugs. Clin
Pharmacol Ther 2018; 103: 271-80.
13. Pilla Reddy V, Bui K, Scarfe G, Zhou D, Learoyd M. Physiologically
Based Pharmacokinetic Modeling for Olaparib Dosing Recommendations:
Bridging Formulations, Drug Interactions, and Patient Populations. Clin
Pharmacol Ther 2019; 105: 229-41.
14. Gaohua L, Wedagedera J, Small BG, Almond L, Romero K, Hermann D,
Hanna D, Jamei M, Gardner I. Development of a Multicompartment
Permeability-Limited Lung PBPK Model and Its Application in Predicting
Pulmonary Pharmacokinetics of Antituberculosis Drugs. CPT
Pharmacometrics Syst Pharmacol 2015; 4: 605-13.
15. (ASHP) ASoH-SP. Assessment of Evidence for COVID-19-Related
Treatments.
<https://www.ashp.org/-/media/assets/pharmacy-practice/resource-centers/Coronavirus/docs/ASHP-COVID-19-Evidence-Table>.
16. IQ. Industry Perspectives on Approaches to Evaluate the Effect of
Renal Impairment on Drug Exposure. 2018.
17. Rowland Yeo K, Zhang M, Pan X, Ban Ke A, Jones HM, Wesche D, Almond
LM. Impact of disease on plasma and lung exposure of chloroquine,
hydroxy-chloroquine and azithromycin: application of PBPK modelling.
Clin Pharmacol Ther 2020.
18. Williams SJ, Baird-Lambert JA, Farrell GC. Inhibition of
theophylline metabolism by interferon. Lancet 1987; 2: 939-41.
19. Schmitt C, Kuhn B, Zhang X, Kivitz AJ, Grange S. Disease-drug-drug
interaction involving tocilizumab and simvastatin in patients with
rheumatoid arthritis. Clin Pharmacol Ther 2011; 89: 735-40.
20. Lee EB, Daskalakis N, Xu C, Paccaly A, Miller B, Fleischmann R,
Bodrug I, Kivitz A. Disease-Drug Interaction of Sarilumab and
Simvastatin in Patients with Rheumatoid Arthritis. Clin Pharmacokinet
2017; 56: 607-15.
21. Keller R, Klein M, Thomas M, Drager A, Metzger U, Templin MF, Joos
TO, Thasler WE, Zell A, Zanger UM. Coordinating Role of RXRalpha in
Downregulating Hepatic Detoxification during Inflammation Revealed by
Fuzzy-Logic Modeling. PLoS Comput Biol 2016; 12: e1004431.
22. Roytblat L, Rachinsky M, Fisher A, Greemberg L, Shapira Y,
Douvdevani A, Gelman S. Raised interleukin-6 levels in obese patients.
Obes Res 2000; 8: 673-5.
23. Hidaka T, Suzuki K, Kawakami M, Okada M, Kataharada K, Shinohara T,
Takamizawa-Matsumoto M, Ohsuzu F. Dynamic changes in cytokine levels in
serum and synovial fluid following filtration leukocytapheresis therapy
in patients with rheumatoid arthritis. J Clin Apher 2001; 16: 74-81.
24. Arican O, Aral M, Sasmaz S, Ciragil P. Serum levels of TNF-alpha,
IFN-gamma, IL-6, IL-8, IL-12, IL-17, and IL-18 in patients with active
psoriasis and correlation with disease severity. Mediators Inflamm 2005;
2005: 273-9.
25. Ataseven H, Bahcecioglu IH, Kuzu N, Yalniz M, Celebi S, Erensoy A,
Ustundag B. The levels of ghrelin, leptin, TNF-alpha, and IL-6 in liver
cirrhosis and hepatocellular carcinoma due to HBV and HDV infection.
Mediators Inflamm 2006; 2006: 78380.
26. Fan J, Zhang X, Liu J, Yang Y, Zheng N, Liu Q, Bergman K, Reynolds
K, Huang SM, Zhu H, Wang Y. Connecting hydroxychloroquine in vitro
antiviral activity to in vivo concentration for prediction of antiviral
effect: a critical step in treating COVID-19 patients. Clin Infect Dis
2020.
27. Jeon S, Ko M, Lee J, Choi I, Byun SY, Park S, Shum D, Kim S.
Identification of antiviral drug candidates against SARS-CoV-2 from
FDA-approved drugs. Antimicrob Agents Chemother 2020.
28. Ufuk A, Assmus F, Francis L, Plumb J, Damian V, Gertz M, Houston JB,
Galetin A. In Vitro and in Silico Tools To Assess Extent of Cellular
Uptake and Lysosomal Sequestration of Respiratory Drugs in Human
Alveolar Macrophages. Mol Pharm 2017; 14: 1033-46.
29. Pilla Reddy V, Walker M, Sharma P, Ballard P, Vishwanathan K.
Development, Verification, and Prediction of Osimertinib Drug-Drug
Interactions Using PBPK Modeling Approach to Inform Drug Label. CPT
Pharmacometrics Syst Pharmacol 2018; 7: 321-30.
30. Shebley M, Sandhu P, Emami Riedmaier A, Jamei M, Narayanan R, Patel
A, Peters SA, Reddy VP, Zheng M, de Zwart L, Beneton M, Bouzom F, Chen
J, Chen Y, Cleary Y, Collins C, Dickinson GL, Djebli N, Einolf HJ,
Gardner I, Huth F, Kazmi F, Khalil F, Lin J, Odinecs A, Patel C, Rong H,
Schuck E, Sharma P, Wu SP, Xu Y, Yamazaki S, Yoshida K, Rowland M.
Physiologically Based Pharmacokinetic Model Qualification and Reporting
Procedures for Regulatory Submissions: A Consortium Perspective. Clin
Pharmacol Ther 2018; 104: 88-110.
31. Taskar KS, Pilla Reddy V, Burt H, Posada MM, Varma M, Zheng M, Ullah
M, Emami Riedmaier A, Umehara KI, Snoeys J, Nakakariya M, Chu X, Beneton
M, Chen Y, Huth F, Narayanan R, Mukherjee D, Dixit V, Sugiyama Y,
Neuhoff S. Physiologically-Based Pharmacokinetic Models for Evaluating
Membrane Transporter Mediated Drug-Drug Interactions: Current
Capabilities, Case Studies, Future Opportunities, and Recommendations.
Clin Pharmacol Ther 2020; 107: 1082-115.
32. Verscheijden LFM, van der Zanden TM, van Bussel LPM, de Hoop-Sommen
M, Russel FGM, Johnson TN, de Wildt SN. Chloroquine dosing
recommendations for pediatric COVID-19 supported by modeling and
simulation. Clin Pharmacol Ther 2020.
33. Cao B, Wang Y, Wen D, Liu W, Wang J, Fan G, Ruan L, Song B, Cai Y,
Wei M, Li X, Xia J, Chen N, Xiang J, Yu T, Bai T, Xie X, Zhang L, Li C,
Yuan Y, Chen H, Li H, Huang H, Tu S, Gong F, Liu Y, Wei Y, Dong C, Zhou
F, Gu X, Xu J, Liu Z, Zhang Y, Li H, Shang L, Wang K, Li K, Zhou X, Dong
X, Qu Z, Lu S, Hu X, Ruan S, Luo S, Wu J, Peng L, Cheng F, Pan L, Zou J,
Jia C, Wang J, Liu X, Wang S, Wu X, Ge Q, He J, Zhan H, Qiu F, Guo L,
Huang C, Jaki T, Hayden FG, Horby PW, Zhang D, Wang C. A Trial of
Lopinavir-Ritonavir in Adults Hospitalized with Severe Covid-19. N Engl
J Med 2020; 382: 1787-99.
34. Pilla Reddy V, Anjum R, Grondine M, Smith A, Bhavsar D, Barry E,
Guichard SM, Shao W, Kettle JG, Brown C, Banks E, Jones RDO. The
pharmacokinetic-pharmacodynamic (PKPD) relationships of AZD3229, a novel
and selective inhibitor of cKIT, in a range of mouse xenograft models of
GIST. Clin Cancer Res 2020.
35. Retallack H, Di Lullo E, Arias C, Knopp KA, Laurie MT,
Sandoval-Espinosa C, Mancia Leon WR, Krencik R, Ullian EM, Spatazza J,
Pollen AA, Mandel-Brehm C, Nowakowski TJ, Kriegstein AR, DeRisi JL. Zika
virus cell tropism in the developing human brain and inhibition by
azithromycin. Proc Natl Acad Sci U S A 2016; 113: 14408-13.
36. Gielen V, Johnston SL, Edwards MR. Azithromycin induces anti-viral
responses in bronchial epithelial cells. Eur Respir J 2010; 36: 646-54.
37. Bacharier LB, Guilbert TW, Mauger DT, Boehmer S, Beigelman A,
Fitzpatrick AM, Jackson DJ, Baxi SN, Benson M, Burnham CD, Cabana M,
Castro M, Chmiel JF, Covar R, Daines M, Gaffin JM, Gentile DA, Holguin
F, Israel E, Kelly HW, Lazarus SC, Lemanske RF, Jr., Ly N, Meade K,
Morgan W, Moy J, Olin T, Peters SP, Phipatanakul W, Pongracic JA, Raissy
HH, Ross K, Sheehan WJ, Sorkness C, Szefler SJ, Teague WG, Thyne S,
Martinez FD. Early Administration of Azithromycin and Prevention of
Severe Lower Respiratory Tract Illnesses in Preschool Children With a
History of Such Illnesses: A Randomized Clinical Trial. JAMA 2015; 314:
2034-44.
38. USA FDA. HIGHLIGHTS OF PRESCRIBING INFORMATION: XOFLUZATM (baloxavir
marboxil) tablets, for oral use. In: FDA US Food & Drug Administration,
October 2019.
39. Yao X, Ye F, Zhang M, Cui C, Huang B, Niu P, Liu X, Zhao L, Dong E,
Song C, Zhan S, Lu R, Li H, Tan W, Liu D. In Vitro Antiviral Activity
and Projection of Optimized Dosing Design of Hydroxychloroquine for the
Treatment of Severe Acute Respiratory Syndrome Coronavirus 2
(SARS-CoV-2). Clin Infect Dis 2020.
40. Liu J, Cao R, Xu M, Wang X, Zhang H, Hu H, Li Y, Hu Z, Zhong W, Wang
M. Hydroxychloroquine, a less toxic derivative of chloroquine, is
effective in inhibiting SARS-CoV-2 infection in vitro. Cell Discov 2020;
6: 16.
41. Moon S, Lee S, Kim H, Freitas-Junior LH, Kang M, Ayong L, Hansen MA.
An image analysis algorithm for malaria parasite stage classification
and viability quantification. PLoS One 2013; 8: e61812.
42. Han S, Hagan DL, Taylor JR, Xin L, Meng W, Biller SA, Wetterau JR,
Washburn WN, Whaley JM. Dapagliflozin, a selective SGLT2 inhibitor,
improves glucose homeostasis in normal and diabetic rats. Diabetes 2008;
57: 1723-9.
43. Lv Z, Chu Y, Wang Y. HIV protease inhibitors: a review of molecular
selectivity and toxicity. HIV AIDS (Auckl) 2015; 7: 95-104.
44. Fintelman-Rodrigues N, Sacramento CQ, Lima CR, da Silva FS, Ferreira
AC, Mattos M, de Freitas CS, Soares VC, Gomes Dias SdS, Temerozo JR,
Miranda M, Matos AR, Bozza FA, Carels N, Alves CR, Siqueira MM, Bozza
PT, Souza TML. 2020.
45. Richardson P, Griffin I, Tucker C, Smith D, Oechsle O, Phelan A,
Stebbing JJL. Baricitinib as potential treatment for 2019-nCoV acute
respiratory disease. 2020; 395: e30.
46. Roskoski R, Jr. Janus kinase (JAK) inhibitors in the treatment of
inflammatory and neoplastic diseases. Pharmacol Res 2016; 111: 784-803.
47. Warren TK, Jordan R, Lo MK, Ray AS, Mackman RL, Soloveva V, Siegel
D, Perron M, Bannister R, Hui HC, Larson N, Strickley R, Wells J,
Stuthman KS, Van Tongeren SA, Garza NL, Donnelly G, Shurtleff AC,
Retterer CJ, Gharaibeh D, Zamani R, Kenny T, Eaton BP, Grimes E, Welch
LS, Gomba L, Wilhelmsen CL, Nichols DK, Nuss JE, Nagle ER, Kugelman JR,
Palacios G, Doerffler E, Neville S, Carra E, Clarke MO, Zhang L, Lew W,
Ross B, Wang Q, Chun K, Wolfe L, Babusis D, Park Y, Stray KM, Trancheva
I, Feng JY, Barauskas O, Xu Y, Wong P, Braun MR, Flint M, McMullan LK,
Chen SS, Fearns R, Swaminathan S, Mayers DL, Spiropoulou CF, Lee WA,
Nichol ST, Cihlar T, Bavari S. Therapeutic efficacy of the small
molecule GS-5734 against Ebola virus in rhesus monkeys. Nature 2016;
531: 381-5.
48. McMullan LK, Flint M, Chakrabarti A, Guerrero L, Lo MK, Porter D,
Nichol ST, Spiropoulou CF, Albarino C. Characterisation of infectious
Ebola virus from the ongoing outbreak to guide response activities in
the Democratic Republic of the Congo: a phylogenetic and in vitro
analysis. Lancet Infect Dis 2019; 19: 1023-32.
49. Siegel D, Hui HC, Doerffler E, Clarke MO, Chun K, Zhang L, Neville
S, Carra E, Lew W, Ross B, Wang Q, Wolfe L, Jordan R, Soloveva V, Knox
J, Perry J, Perron M, Stray KM, Barauskas O, Feng JY, Xu Y, Lee G,
Rheingold AL, Ray AS, Bannister R, Strickley R, Swaminathan S, Lee WA,
Bavari S, Cihlar T, Lo MK, Warren TK, Mackman RL. Discovery and
Synthesis of a Phosphoramidate Prodrug of a
Pyrrolo[2,1-f][triazin-4-amino] Adenine C-Nucleoside (GS-5734)
for the Treatment of Ebola and Emerging Viruses. J Med Chem 2017; 60:
1648-61.
50. Yu R, Song D, DuBois DC, Almon RR, Jusko WJ. Modeling Combined
Anti-Inflammatory Effects of Dexamethasone and Tofacitinib in Arthritic
Rats. AAPS J 2019; 21: 93.
51. Loew D, Schuster O, Graul EH. Dose-dependent pharmacokinetics of
dexamethasone. Eur J Clin Pharmacol 1986; 30: 225-30.
52. USA FDA. <Tocilizumab FDA label.pdf>.
53. Abdallah H, Hsu JC, Lu P, Fettner S, Zhang X, Douglass W, Bao M,
Rowell L, Burmester GR, Kivitz A. Pharmacokinetic and Pharmacodynamic
Analysis of Subcutaneous Tocilizumab in Patients With Rheumatoid
Arthritis From 2 Randomized, Controlled Trials: SUMMACTA and BREVACTA. J
Clin Pharmacol 2017; 57: 459-68.
54. Mihara M, Ohsugi Y, Kishimoto T. Tocilizumab, a humanized
anti-interleukin-6 receptor antibody, for treatment of rheumatoid
arthritis. Open Access Rheumatol 2011; 3: 19-29.
55. Khan F, Fabbri L, Stewart I, Robinson K, Smyth AR, Jenkins G. 2020.
56. Hensley LE, Fritz LE, Jahrling PB, Karp CL, Huggins JW, Geisbert TW.
Interferon-beta 1a and SARS coronavirus replication. Emerg Infect Dis
2004; 10: 317-9.
57. USA FDA, Anakinra.
https://www.accessdata.fda.gov/drugsatfda_docs/nda/2001/103950-0_Kineret_Biopharmr.PDF.
2001.
58. USA FDA, Siltuximab.
https://www.accessdata.fda.gov/drugsatfda_docs/nda/2014/125496Orig1s000MedR.pdf.
59. USA FDA. Highlights of prescribing information for Calquence
(acalabrutinib) capsules, for oral use. In, 2017.
60. USA FDA. Highlights of prescribing information for Zithromax
(azithromycin) for IV infusion only. In, 2017.
61. USA FDA. Highlights of prescribing information: Olumiant
(baricitinib) tablets, for oral use. In, 2018.
62. USA FDA. Highlights of Prescribing Information: Xofluza (baloxavir
marboxil). In, 2018.
63. USA FDA. ARALEN Chloroquine Phosphate, USP Label. In, 2018.
64. USA FDA. PLAQUENIL Hydroxychloroquine sulfate tablets, USP. In,
2019.
65. USA FDA Darunavir.
https://www.accessdata.fda.gov/drugsatfda_docs/label/2017/021976s045_202895s020lbl.pdf.
2017.
66. USA FDA. Highlights of prescribing information for FARXIGA
(dapagliflozin). In, January 2020.
67. de Zwart L, Snoeys J, De Jong J, Sukbuntherng J, Mannaert E,
Monshouwer M. Ibrutinib Dosing Strategies Based on Interaction Potential
of CYP3A4 Perpetrators Using Physiologically Based Pharmacokinetic
Modeling. Clin Pharmacol Ther 2016; 100: 548-57.
68. USA FDA, Ibrutinib.
https://www.accessdata.fda.gov/drugsatfda_docs/label/2018/210563s000lbl.pdf.
2018.
69. USA FDA. Fact sheet for health care providers: Emergency use
authorization (EUA) of Remdesivir (GS-5734). In, 2020, May 1.
70. USA FDA. Highlights of prescribing information for JAKAFI
(ruxolitinib) tablets, for oral use. In, 2011.
71. USA FDA. Highlights of prescribing information for NORVIR
(ritonavir) tablet, for oral use/ oral solution/ oral powder. In, 2017.
72. USA FDA. NDA/BLA Multi-disciplinary Review and Evaluation {NDA
210259} {CALQUENCE, acalabrutinib}. In, edResearch CfDEa, FDA US Food
& Drug Administration, 2016, February 1.
73. de Jong J, Skee D, Hellemans P, Jiao J, de Vries R, Swerts D, Lawitz
E, Marbury T, Smith W, Sukbuntherng J, Mannaert E. Single-dose
pharmacokinetics of ibrutinib in subjects with varying degrees of
hepatic impairment<sup/>. Leuk Lymphoma 2017; 58:
185-94.
74. Mazzei T, Surrenti C, Novelli A, Crispo A, Fallani S, Carla V,
Surrenti E, Periti P. Pharmacokinetics of azithromycin in patients with
impaired hepatic function. J Antimicrob Chemother 1993; 31 Suppl E:
57-63.
75. Gustafsson LL, Walker O, Alvan G, Beermann B, Estevez F, Gleisner L,
Lindstrom B, Sjoqvist F. Disposition of chloroquine in man after single
intravenous and oral doses. Br J Clin Pharmacol 1983; 15: 471-9.
76. Tett SE, Cutler DJ, Day RO, Brown KF. Bioavailability of
hydroxychloroquine tablets in healthy volunteers. Br J Clin Pharmacol
1989; 27: 771-9.
77. Baricitinib. NDA/BLA Multi-disciplinary Review and Evaluation {NDA
20792} {Baricitinib}.
78. Peng JZ, Pulido F, Causemaker SJ, Li J, Lorenzo A, Cepeda C, Garcia
Cabanillas JA, DaSilva B, Brun SC, Arribas J. Pharmacokinetics of
lopinavir/ritonavir in HIV/hepatitis C virus-coinfected subjects with
hepatic impairment. J Clin Pharmacol 2006; 46: 265-74.
79. USA FDA Atazanavir.
https://www.accessdata.fda.gov/drugsatfda_docs/label/2005/021567s005lbl.pdf.
80. Sekar V, Spinosa-Guzman S, De Paepe E, Stevens T, Tomaka F, De Pauw
M, Hoetelmans RM. Pharmacokinetics of multiple-dose darunavir in
combination with low-dose ritonavir in individuals with mild-to-moderate
hepatic impairment. Clin Pharmacokinet 2010; 49: 343-50.
81. Chen X, Shi JG, Emm T, Scherle PA, McGee RF, Lo Y, Landman RR,
Punwani NG, Williams WV, Yeleswaram S. Pharmacokinetics and
pharmacodynamics of orally administered ruxolitinib (INCB018424
phosphate) in renal and hepatic impairment patients. Clin Pharmacol Drug
Dev 2014; 3: 34-42.
82. USA FDA. Highlights of prescribing information for Dexamethsone
(Hemady).
83. Hoffler D, Koeppe P, Paeske B. Pharmacokinetics of azithromycin in
normal and impaired renal function. Infection 1995; 23: 356-61.
84. Salako LA, Walker O, Iyun AO. Pharmacokinetics of chloroquine in
renal insufficiency. Afr J Med Med Sci 1984; 13: 177-82.
85. Kasichayanula S, Liu X, Pe Benito M, Yao M, Pfister M, LaCreta FP,
Humphreys WG, Boulton DW. The influence of kidney function on
dapagliflozin exposure, metabolism and pharmacodynamics in healthy
subjects and in patients with type 2 diabetes mellitus. Br J Clin
Pharmacol 2013; 76: 432-44.
86. Lopinavir.
https://liverpool-hiv-hep.s3.amazonaws.com/prescribing_resources/pdfs/000/000/073/original/HIV_FactSheet_LPV_2016_Mar.pdf?1520612690
2016