This commentary seeks to share some key insights relevant to optimizing COVID‐19 candidate
therapeutics that were learned from attempts to optimize anti‐infective posology in
settings where quality and timely availability of data is challenging, with particular
focus on influenza, including experiences from H5N1 and pH1N1 outbreaks.
As of March 18, 2020, severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2),
the virus that causes the COVID‐19 disease, has infected > 190,000 people globally
and caused > 7,800 deaths, with > 90,000 confirmed infections in the Western Pacific
Region and > 3,300 deaths and > 74,000 confirmed cases in the European region with
> 3,300 deaths. Estimates of case fatality rate and infectivity place SARS‐CoV‐2 and
COVID‐19 as a significant global threat with the World Health Organization (WHO) risk
assessment of very high at a global level and on March 11 was a declaration of a pandemic.
1
It is very likely that the actual number of infections and cases are higher, perhaps
significantly, as a result of reporting practices and lack of test availability in
many countries.
Emerging infectious pathogens are incredibly efficient at evolving rapidly, with infectivity
and virulence optimized for their own promulgation, governed by laws of Darwinism.
Juxtaposed on top of the nimbleness of an emerging pandemic pathogen, is the sluggish
human response for martialing a public health response to such a crisis. In an effort
to find therapeutics where none currently exist, the current response to COVID‐19
largely centers around the execution of product development plans developed for noncrisis
times.
It Doesn’t Seem Like a Fair Fight!
With COVID‐19 vaccine availability likely 12–18 months away, there is an immediate
need to examine COVID‐19 therapeutic candidate options, which could arrive on the
scene more rapidly. In the race to move forward with urgency, it is essential to ensure
optimal posology of these drugs in this new indication. It is imperative to consider
a drug’s activity against the pathogen of interest, as it is critical that the concentrations
obtained at the site(s) of action are sufficient to result in viral inhibition. If
the antiviral activity (e.g., half‐maximal inhibitory concentration) of a drug approved
for use against another virus differs against SARS‐CoV‐2, the standard dosing regimen
may not be sufficient. Beyond this, it is important to consider differences in pharmacokinetics
(PK), which may impact dosing for different patient populations, including treatment
of infected patients as well as prophylaxis of healthcare workers and contacts and,
of course, for different patient subpopulations, including children, elderly, and
those with renal and hepatic impairment and on concomitant medications. It is not
uncommon for young healthy individuals (such as first responders) to have more rapid
drug clearance and lower exposures of a drug compared with the approved patient population.
In such cases, there may be a risk of underdosing, depending on the drug. On the other
hand, antiretroviral drugs being considered for use against this pathogen have been
largely dosed to younger patients rather than the older population who are at greater
risk for severe disease. Their physiological differences also must be considered in
developing effective treatment regimens.
Beyond PK, it is also important to consider intrinsic and extrinsic factors, which
may impact pharmacodynamics (PD), including patients who are immunocompromised who
may require longer durations of therapy.
In pandemic situations, drug supply is often compromised. In the setting of limited
stockpiles, optimal posology is critical, as every milligram above optimal means a
potential patient or healthcare worker may go untreated. From a public health perspective,
this equates to resources that could have been deployed to other interventions and
may be considered wasted. Every milligram below optimal may potentially result in
the emergence of resistance, reduced susceptibility, and reduced efficacy to therapeutic
interventions.
This commentary seeks to share some key insights relevant to optimizing posology of
COVID‐19 candidate therapeutics that were learned from attempts to optimize anti‐infective
posology in settings where quality and timely availability of data are challenging,
with particular focus on influenza, including experiences from H5N1 and pH1N1 outbreaks.
Insight 1:
It is critical that clinical pharmacology and optimal posology need to be deeply integrated
into the evaluation and determination of COVID‐19 candidate therapeutic options.
This insight is not at all obvious for many clinical investigators, public health
officials, and other stakeholders who seek to establish recommendations for use of
candidate therapeutics against COVID‐19. As of March 1, 2020, excluding suspended,
terminated, or completed trials, there were 24 interventional “drug” clinical trials
to treat coronavirus disease listed on ClinicalTrials.gov. The primary focus of such
trials is the comparative efficacy of single and combination‐dose regimens of therapeutic
interventions or standard of care. Even though the full details of the protocols are
not available for many trials on the website, it is striking that none of the trials
seem to examine dose ranging of the therapeutic interventions within the designs.
Furthermore, it is not apparent whether PK or exposure‐response is being evaluated
in any of the trials, and the interventions focus on evaluating treatment interventions
of infected patients rather than prophylaxis.
2
Even if an intervention seems more efficacious than another intervention or standard
of care in one of the trials, it seems that the current clinical trials underway,
as listed on ClinicalTrials.gov, provide no insight into dose‐response and, therefore,
no guidance on optimal dosing of a specific therapeutic intervention for COVID‐19.
A WHO‐sponsored COVID‐19 master protocol evaluating investigational therapeutics for
treatment of COVID‐19 in hospitalized patients does include a provision for obtaining
PK and performing exposure‐response analyses.
3
In general, there is little known about antiviral clinical pharmacology against COVID‐19.
Many COVID‐19 antiviral candidates have complex pharmacology ranging from nucleoside
prodrug conversion to nucleotide (remdesivir) to PK enhancement (lopinavir/ritonavir
and darunavir/cobicistat), and complex drug‐drug interaction profiles, including CYP3A4
auto‐induction and inhibition. In order to mitigate the potential for incorrectly
selecting a dose for further evaluation, it is recommended that clinical trials with
candidate therapeutics advance the suggestions above by the WHO and consider the following:
include additional dose regimen arms, collect PK, conduct exposure‐response analyses,
and apply modeling and simulation. These recommendations are equally relevant for
investigational studies evaluating prophylaxis. Ideally, the data from these multitude
of small trials will be made available in a useable format to allow pooling of data
and more informative analyses.
Also, of note, the current clinical trials underway, as listed on ClinicalTrials.gov,
are relatively restrictive based on their study populations (i.e., primarily adults
without significant renal or hepatic impairment). It is suggested that the clinical
and quantitative pharmacology community engage with COVID‐19 investigators and other
stakeholders to ensure trials and programs take into account clinical pharmacology
fundamentals, such as dosing requirements for patient subpopulations, including pediatrics,
elderly, pregnancy, and those with renal or hepatic impairment, or receiving concomitant
medications.
Without conscious and deep integration of clinical pharmacology into the evaluation
and determination of COVID‐19 candidate therapeutic options, there is a risk of repeating
experiences with 2014–2015 Ebola epidemic, where none of the therapeutic trials ended
with conclusive results on product efficacy, and some of the inconclusive trials may
have actually set back the search for safe and effective therapeutics.
4
Insight 2:
Contingencies need to be built in to support decision making in the COVID‐19 epidemic
given that randomized controlled trials (RCTs) may not provide definitive outcomes.
At the WHO novel coronavirus forum held on February 11–12, 2020, it was apparent that
there is much still to learn about COVID‐19. Fundamental information, such as the
virus, its transmissibility and virulence, patient groups impacted, and clinical manifestations
of the disease, might be changing. There is little known about the time‐course of
viral shedding in the respiratory tract, blood, and gastrointestinal tract, the relative
importance for each for an antiviral regimen, or the potential treatment window when
an antiviral may be effective or not effective. There is also an absence of information
on the potential for COVID‐19 to generate resistance to antivirals as well as agreed
upon clinical trial end points, which are the subject of learning and refinement in
the WHO master protocol. Due to the rapidly evolving nature of the information, therapeutic
interventions and RCT designs will continue to be honed and lead to data quality challenges
and critical assumptions underpinning comparative efficacy assessments from such trials.
Thus, in parallel to the RCTs, it is essential that additional decision quality data
are identified both as a risk mitigation approach but also to augment information
generated from RCTs.
There is an opportunity to leverage emergency and compassionate use programs of candidate
COVID‐19 therapeutics to collect useful clinical pharmacology data to inform posology.
Traditionally, emergency and compassionate use programs collect crude information
that is not rigorous enough to gather insights or support decision making (e.g., emergency
and compassionate use of intravenous oseltamivir phosphate during the pH1N1 influenza
pandemic).
5
In contrast, an innovative open‐label compassionate use program was conducted for
linezolid, an antibacterial with activity against multidrug‐resistant gram‐positive
pathogens, with the rigors of an open‐label clinical trial and incorporated PK, PK/PD,
and modeling and simulation. The PK/PD trial was able to demonstrate drug effect and
optimal dosing via PK/PD associations from < 288 patients who received a single 600 mg
b.i.d. dose of linezolid.
6
The broader program was able to provide many other important insights on PK and response
to difficult‐to‐treat infection sites and pathogens, safety, and emergence of resistance
that are featured in the product label. The intrinsic PK/PD variability in this trial,
along with the quantitative analyses applied, were powerful features in being able
to uncover important insights for linezolid dosing. Such an approach could be very
relevant to support optimal dosing of COVID‐19 therapeutic candidates, augmenting
RCT efforts.
The utility of translational PK/PD models extends, of course, far beyond finding the
drug effect from variable data environments, to strategically providing efficiencies
in overarching development programs and clinical trial design. For example, a Bayesian
clinical study design informed by priors from translational PK/PD models could minimize
the number of patients that will need to be tested in a clinical trial, and the totality
of data can then be analyzed using an integrated exposure‐response model to substantiate
evidence of efficacy. Akin to the animal rule, in situations of disparate and emerging
data, such as in the COVID‐19 pandemic, there also exists significant opportunity
for the “model‐informed totality of evidence” approach in supporting regulatory decision
making for accelerating access to COVID‐19 therapeutics.
Another opportunity is to develop a precompetitive in silico workbench to aggregate
existing and emerging information on COVID‐19 candidate therapeutics from in vitro,
preclinical, and clinical data sources.
Such an in silico workbench making use of intuitive data visualization software, such
as R‐Shiny, could be made available to investigators and experts as a tool to integrate
and share state of the art information to guide decision making on incorporating candidate
therapeutics into clinical trial evaluations and guidance’s for use during the COVID‐19
outbreak.
An additional contingency opportunity to help inform COVID‐19 optimal posology should
be a work stream focused on aggregating emerging real‐world evidence and clinical
trials data. Specifically, the application of meta‐analytic methodologies, like model‐based
meta‐analyses, provides an opportunity to mine insights on interventions (and their
posology) from across pooled trials, which in of themselves may not have adequate
information content to definitively address specific hypotheses.
In a rapidly emerging situation, much can be learned from prior experience with similar
molecules. For example, knowing the pattern of viral kinetics for the new pathogen
can provide critical insights for therapeutic intervention, including determination
of the window of opportunity to initiate therapy (antivirals for respiratory diseases
are ineffective if administered after the time of peak viral load), potential utility
of loading doses to rapidly achieve therapeutic levels, and to inform the necessary
duration of treatment to maximize antiviral response.
7
The application of quantitative and clinical pharmacology principles described above
to distill information content from “gray” and emerging information remains highly
relevant to accelerate COVID‐19 therapeutics across a range of treatment modalities,
including monoclonal antibodies, plasma derived therapeutics, small molecules, and
complex biologics.
Insight 3:
Emerging data and potential solutions must be examined and presented from the perspective
of different stakeholders.
It is essential to recognize that therapeutic intervention goals for an individual
patient can diverge significantly from public health goals and differ to that from
procurers. This means the concept of optimal posology of a COVID‐19 candidate therapeutic
can take on many different personas depending upon the stakeholder. A treating physician
may be interested in how to improve the clinical outcome of the infected patient and
exposed individuals; a public health professional may be interested in minimizing
viral shedding to reduce spread of COVID‐19, and a procurer may be focused on where
to invest healthcare dollars in order to maximize impact of the entire healthcare
system. This subject has been exhaustively explored for pandemic planning scenarios
for oseltamivir posology against a range of potential influenza virus strains, differing
by infectivity and virulence (Table
1
)
8
The interdisciplinary linkage of PK/PD, epidemiological, and health economic models
into a single quantitative framework offers an objective way for stakeholders to engage
earlier in cross‐sector dialogue on dosing, procurement, and deployment strategies.
This should have particular relevance for COVID‐19 candidate therapeutics where PK/PD
information may exist, but clinical evidence is sparse or lagging.
Table 1
Integration of oseltamivir PK/PD, epidemiology, and health economics to support influenza
pandemic planning decisions for multiple stakeholders; reprinted with permission;
Kamal et al.
8
Comparators (treatment vs. baseline)
Costs (A) (payer)
Costs (B) (payer – baseline)
Costs (A) (societal)
Costs (B) (societal ‐ baseline)
Death (A)
Death (B)
Δ Death (A–B)
Δ LYs (A–B)
Δ QALYs (A–B)
Payer perspective
Societal perspective
Cost per LY gained
Cost per QALY gained
Cost per LY gained
Cost per QALY gained
Low transmissibility and low severity
75 mg (A) vs. no treatment (B)
9,225,251
42,578,018
12,998,947
106,995,703
27
439
−412
399
430
Cost‐saving
Cost‐saving
Cost‐saving
Cost‐saving
150 mg (A) vs. 75 (B) mg
14,835,713
9,225,251
17,109,649
12,998,947
16
27
−11
10
11
546,753
515,260
400,598
377,524
High transmissibility and high severity
75 mg (A) vs. no treatment (B)
94,961,869
144,271,547
171,053,550
272,957,742
974
1591
−617
598
629
Cost‐saving
Cost‐saving
Cost‐saving
Cost‐saving
150 mg (A) vs. 75 mg(B)
81,019,150
94,961,869
139,379,855
171,053,550
747
974
−227
220
227
Cost‐saving
Cost‐saving
Cost‐saving
Cost‐saving
Low transmissibility and high severity
75 mg (A) vs. no treatment (B)
11,450,971
79,213,439
15,596,974
149,869,617
53
874
−821
795
828
Cost‐saving
Cost‐saving
Cost‐saving
Cost‐saving
150 mg (A) vs. 75 mg (B)
16,176,877
11,450,971
18,675,157
15,596,974
32
53
−21
20
21
231 280
223 797
150 642
145 768
High transmissibility and low severity
75 mg (A) vs. no treatment (B)
54,113,197
77,547,403
123,371,900
194,871,423
489
799
−310
300
330
Cost‐saving
Cost‐saving
Cost‐saving
Cost‐saving
150 mg (A) vs. 75 mg (B)
49,689,085
54,113,197
102,809,041
123,371,900
375
489
−114
110
117
Cost‐saving
Cost‐saving
Cost‐saving
Cost‐saving
All costs are expressed in 2013 US dollars.
A, the alternative intervention; B, the baseline intervention.
LY, life year; PD, pharmacodynamic; PK, pharmacokinetic; QALY, quality‐adjusted life
year.
John Wiley & Sons, Ltd
Every experiment represents an opportunity to gather information content in clinical
and quantitative pharmacology, clinical efficacy, and safety, as well as insights
in epidemiology and health economics. For immediate application to COVID‐19 candidate
therapeutics, the recommendation is to apply considerable fungibility in thinking
and look for opportunities to collect information that will strengthen the evidence
base to support insights for multiple audiences.
Insight 4:
Multi‐sectoral collaboration and coordination is critical to gather the right information
for the right stakeholder at the right time.
Front‐line physicians and clinical investigators, academic and scientific experts,
epidemiology and public health practitioners, government procurers, not for profit
and access stakeholders, patient advocates, and industry experienced drug developers
all have essential, unique, and complementary competencies and perspectives to provide
in the development and implementation of candidate therapeutics for COVID‐19.
Currently, there is an urgent need for global leadership that recognizes how to assemble
and coordinate the requisite competencies in an appropriate governance framework in
order to focus on evaluation and determination of candidate therapeutics for COVID‐19
and to establish a framework for responding to future pathogens beyond COVID‐19. Some
high‐level considerations for an effective global coalition to accelerate therapeutics
for COVID‐19 include the importance of hyper‐transparent data sharing as well as applying
findability, accessibility, interoperability, and reusability (FAIR) guiding principles
for scientific data management and stewardship.
9
In addition, it is essential that drug development principles apply advanced data
science and contemporary development methods, including model‐informed drug development
methods, not to cut corners in drug development, but to increase parallel processing
and making most of the available information content, to cut time and accelerate the
development and access of critical medicines.
Let’s not repeat this exercise in a few years’ time.
“The future depends on what we do in the present.” (Mahatma Gandhi).
Funding
No funding was received for this work.
Conflict of Interest
All authors work for Certara, a consulting firm in integrated drug development and
have directly consulted with a variety of not‐for‐profit global health organizations,
biotechnology, and pharmaceutical companies and governments with an interest in medical
countermeasures against respiratory virus infections.