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Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions

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Abstract and Figures

Respiratory disease trials are profoundly affected by non-pharmaceutical interventions (NPIs) against COVID-19 because they perturb existing regular patterns of all seasonal viral epidemics. To address trial design with such uncertainty, we developed an epidemiological model of respiratory tract infection (RTI) coupled to a mechanistic description of viral RTI episodes. We explored the impact of reduced viral transmission (mimicking NPIs) using a virtual population and in silico trials for the bacterial lysate OM-85 as prophylaxis for RTI. Ratio-based efficacy metrics are only impacted under strict lockdown whereas absolute benefit already is with intermediate NPIs (eg. mask-wearing). Consequently, despite NPI, trials may meet their relative efficacy endpoints (provided recruitment hurdles can be overcome) but are difficult to assess with respect to clinical relevance. These results advocate to report a variety of metrics for benefit assessment, to use adaptive trial design and adapted statistical analyses. They also question eligibility criteria misaligned with the actual disease burden. A computational mechanistic viral infection model and trial simulation advocates for adaptation of respiratory disease clinical trials whose chances of success and interpretability are being degraded under COVID-19 pandemic mitigation measures.
Results of in silico clinical trials in prophylaxis of respiratory tract infections (RTIs) with four scenarios of non-pharmaceutical interventions (NPIs) against COVID-19 pandemic with increasing strength (absent - dark purple, mild - light purple, medium - light red, and strong - yellow) modeled by a decrease of the transmission rate parameter (no reduction, −5%, −15% and −25%, respectively) For all scenarios, the simulations are run for 2 years. Year 1 is the selection year during which patients are screened and possibly included in an in silico trial. There is no NPI during year 1. The NPIs are started at the beginning of year 2 as well as the treatment (ten daily administrations of 3.5 mg of OM-85 from the beginning of the month for 3 consecutive months). RTIs are counted for the complete duration of year 2. a Weekly incidence of RTIs per 100,000 is plotted for 2 years of simulations for the four NPI scenarios. b Distribution (interquartile range, IQR) of absolute benefit (number of prevented RTIs in year 2) is plotted for the four NPI scenarios. Absolute benefit can be interpreted as the number of prevented RTIs in year 2 when comparing the treated and the control group. c Distribution (IQR) of event RTI rate ratio (ERR, treated over control group) is plotted for the four NPI scenarios. d Effect Model plot for the four NPI scenarios. Each in silico clinical trial is plotted (symbols) with the number of RTIs in the control group as x-coordinate and the number of RTIs in the treated group as y coordinate. The region of clinically relevant efficacy is indicated in orange. It is defined by at least 1 prevented RTI in absolute benefit (dashed-dotted line), at least 20% reduction in number of RTIs (solid line) and at least 3 RTIs in the control group. e Distribution (IQR) of sample sizes per arm required to show efficacy of OM-85 treatment in reducing number of RTIs for the four NPI scenarios. f Distribution (IQR) of estimated patient screening times under the four NPI scenarios by assuming a hypothetical screening rate of 1000 patients per year and by taking year 2 as the selection year (without treatment). Sensitivity of these results to mechanistic uncertainty is reported in Supplementary Fig. 14.
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ARTICLE
Modeling the disruption of respiratory disease
clinical trials by non-pharmaceutical COVID-19
interventions
Simon Arsène1, Claire Couty 1,4, Igor Faddeenkov1,4, Natacha Go1,4, Solène Granjeon-Noriot1,4, Daniel Šmít1,
Riad Kahoul 1, Ben Illigens1,2, Jean-Pierre Boissel1, Aude Chevalier3, Lorenz Lehr3, Christian Pasquali3&
Alexander Kulesza 1
Respiratory disease trials are profoundly affected by non-pharmaceutical interventions (NPIs)
against COVID-19 because they perturb existing regular patterns of all seasonal viral epi-
demics. To address trial design with such uncertainty, we developed an epidemiological
model of respiratory tract infection (RTI) coupled to a mechanistic description of viral RTI
episodes. We explored the impact of reduced viral transmission (mimicking NPIs) using a
virtual population and in silico trials for the bacterial lysate OM-85 as prophylaxis for RTI.
Ratio-based efcacy metrics are only impacted under strict lockdown whereas absolute
benet already is with intermediate NPIs (eg. mask-wearing). Consequently, despite NPI,
trials may meet their relative efcacy endpoints (provided recruitment hurdles can be
overcome) but are difcult to assess with respect to clinical relevance. These results advo-
cate to report a variety of metrics for benet assessment, to use adaptive trial design and
adapted statistical analyses. They also question eligibility criteria misaligned with the actual
disease burden.
https://doi.org/10.1038/s41467-022-29534-8 OPEN
1Novadiscovery SA, Lyon, France. 2Dresden International University, Dresden, Germany. 3OM Pharma, Meyrin, Switzerland.
4
These authors contributed
equally: Claire Couty, Igor Faddeenkov, Natacha Go, Solène Granjeon-Noriot. email: Alexander.Kulesza@novadiscovery.com
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The COVID-19 pandemic and consecutive response measures
to contain the spread of SARS-CoV-2 in the form of non-
pharmaceutical interventions (NPIs) have changed not only
peopleslifeandhealth
1but also the process of developing vaccines
and potential treatments2. This has led to a rapid pursue of different
immunization strategies against the virus3,4, a surge of drug
repurposing and the screening of new treatment candidates5,6.
Clinical development in non-COVID-19 disease areas, how-
ever, has been substantially impaired7. Due to the high number of
COVID-19 cases during the pandemic in 2020, trial initiation
dropped by up to 30% in the USA8. During the rst wave of the
pandemic, more than 1000 trials were stopped as a consequence9.
Social distancing and quarantine measures have negatively
affected patientsparticipation in clinical trials. The surge in
hospitalizations of COVID-19 patients also affected personnels
capacity to conduct trials10 and has led to incomplete or delayed
data collection in ongoing trials with foreseeable difculties for
patient enrollment and follow-up in upcoming trials. Trialists
expect that the collateral impact of COVID-19 on clinical trials
will persist for several years11, given that intermittent contain-
ment measures are possible beyond the year 202512.
This is especially critical for trials investigating diseases of the
pulmonary system. About 10% of all trials conducted in Europe in
pre-COVID-19 times were on respiratory diseases13,14.Dueto
COVID-19 containment measures that intend to attenuate SARS-
CoV-2 transmission, respiratory disease transmission is altered at
the population scale, and/or there might be under-reporting of
respiratory diseases to healthcare services (see a recent systematic
review by Alqahtani et al.15). Recent reports show that seasonal
dynamics of common respiratory tract infections (RTIs) have almost
vanished during the COVID-19 pandemic1619. In England, overall
fewer cases of common cold, u, and bronchitis have been reported
during the lockdown20. Detection bias due to reduced testing is not
asignicant confounder as several sources worldwide2123 reported
a sharp decline in the number of RTIs relative to the number of
tests. Hospitalization for acute bronchiolitis in children <1 year old
saw a signicant reduction, on the order of 7090%, comparing
2020 with earlier years24. For chronic obstructive pulmonary disease
(COPD, often triggered by viral infections), healthcare professionals
in Europe have reported fewer cases in community and acute hos-
pital settings25,26, and a decline in asthma exacerbations has been
reported as well27. While this decline may be regarded as a positive
side-effect of the pandemic, it is only temporary, and rebounding of
the respiratory disease burden can happen28,29. At the same time,
respiratory disease prophylaxis and trials across the world are
strongly affected by these drastic changes because the design of
clinical trials is usually conceived from pre-pandemic settings, e.g.,
thesamplesizecalculationandthe choice of endpoints and elig-
ibility are based on historical interventional and observational stu-
dies and do not mirror the current pandemic situation. Therefore,
clinical trial feasibility in respiratory diseases remains an open
question in the medium term.
Modeling and simulation might be an approach to address the
lack in representativity of historical data if forecasts of disease
transmission can be joined with clinical trial simulation. For
example, simulated clinical trials have provided the means to
test a multitude of design choices3033 and became a eld gaining
attraction throughout regulatory agencies34.TheCOVID-19
Fig. 1 Multi-scale in silico approach to incorporate within-host and between-host respiratory tract infection (RTI) model as well as a treatment model
with bacterial lysate OM-85. The model is used to assess feasibility of clinical trials in prophylaxis of RTIs during COVID-19 pandemic. The
transmission of the major respiratory pathogens respiratory syncytial virus (RSV), rhinovirus, and inuenza type A and B viruses is given by a seasonal
susceptible, infected, recovered, and again susceptible (SIRS) model (between-host model). This model is interfaced to a within-host immunology model
via a time-dependent instantaneous prevalence of infection triggering or not viral exposure at randomly chosen time points. Individual patients are
identied by their age and an immuno-competence meta-parameter impacting the immune response from which infections are included or omitted from
the cumulative number of infections depending on viremia. To prevent RTIs, virtual patients are treated with the bacterial lysate OM-85, which acts
through a pro-type I immunomodulation mechanism of action and which is described by a physiologically based pharmacokinetics (PBPK) and
pharmacodynamics (PD) treatment model with downstream effects in the immunological model. The impact of COVID-19 associated non-pharmaceutical
interventions (NPIs) are simulated by scaling of the transmission term in the between-host part of the model. Figure created with BioRender.com.
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pandemic has already transformed the modeling and simulation
community. For example, governments rely on mathematical
often epidemiological between-host viral transmissionmodels
to predict the evolution of the pandemic and to take evidence-
based decisions35. On the other hand, viral kinetic modeling,
focusing on the patient immunology and viral infection resolu-
tion, can be used to accelerate drug development36.Asmen-
tioned in a recent review by Karr et al.37, multi-scale within-host
modeling is common, but there are much fewer models that
interface within-host models with between-host models because
the within-host granularity risks getting lost when integrating to
a higher scale. In particular, to our knowledge, there is currently
no available modeling approach that can simulate RTI prophy-
laxis trials under COVID-19 pandemic conditions and, that
could serve to better inform respiratory disease trial design and
clinical development decisions. Based on the applicability of viral
kinetic models on the population and individual scale (i.e.,
immunology) for a broad variety of viruses, we hypothesized that
a mechanistic model as schematized in Fig. 1could be used for in
silico RTI prophylaxis trial simulation and to forecast trial fea-
sibility. After matching known viral disease burden seasonality,
intra- and inter-patient variability in RTI resolution and efcacy
data, we built a mechanistic model and simulated placebo-
controlled in silico trials in 15-year-old pediatric patients with
recurrent RTIs (RRTI) treated with an immunomodulating
bacterial lysate under four different hypotheses of NPI intensities
and assessed efcacy and benetmetricsasafunctionofNPI
intensity. We chose the example of OM-85, which is a well
characterized (Yin et al.38) member of a series of bacterial lysates
containing medicinal products for respiratory conditions that
have been used in over 120 million patients but need to soon
provide new clinical efcacy data in view of an EU referral
procedure39 and explored clinical interpretation, power, sample
size and recruitment considerations as aspects of trial feasibility.
From the simulations, we conclude on COVID-19 pandemic-
related risk mitigation strategies for conrmatory trials con-
cerning this entire class of products and RTI prophylaxis trials in
general.
Results
Effect of NPIs on RTI disease burden. Our epidemiological
model is based on a compartmental approach describing
susceptible, infected, recovered, and again susceptible (SIRS)
individuals and explicitly describes transmission, recovery, and
immunity loss rates (Fig. 2a, Methods). We calibrated this model
in a parallel manner for the main respiratory viruses (Supple-
mentary Fig. 4, note that the vast majority of RTI are considered
to be of viral origin40). As representative comparator and vali-
dation of the prior virus-specic infection dynamics calibration,
we used the 5-year average and the 20192020 upper and lower
RTI (URTI and LRTI) incidence from the communicable and
respiratory disease report 20192020 published in the UK by the
Royal College of General Practitioners (RCGP)41 (points and full
lines in Fig. 2b). To model NPI, starting at week 12 in 2020, we
decreased the scaling factor of the viral transmission rate (b
0
,
Supplementary Methods: Between-host SIRS model) by 17.5% to
reproduce the difference between the unperturbed 5-year average
and the perturbed 20192020 URTI and LRTI incidence with the
lockdown. Results of the simulations are displayed as dashed lines
in Fig. 2b. Simulations and data show a similar strong decline of
the disease incidence with the beginning of the lockdown in the
UK during March 2020 (week 1014)42 while the 20192020
disease burden closely follows the 5-year average (as reported in
Iacobucci et al.20). With the adjusted transmission rate and
otherwise unchanged parameters, the root mean square deviation
(RMSD) for the weekly incidence per 100,000 of the simulation
vs. data are 82 and 96 (unperturbed simulation vs. 5-year average
data and perturbed simulation vs. 20192020, data, respectively),
which is smaller than the variability within the observed data
before lockdown (RMSD of 102 for the 5-year average vs.
20192020 data for the time points considered). Furthermore,
reproduction of RTI incidence broken down into URTIs and
LRTIs (Supplementary Fig. 1) shows convincing capability to
describe the effect of transmission perturbation on RTIs. Sup-
ported by this agreement, we applied this epidemiological model
to modulate the instantaneous hazard of exposure to RTI-causing
viruses in our in silico trials with four different NPI scenarios.
Effect of NPIs on efcacy of RTI prophylaxis. To represent the
effect of different NPI scenarios for a 2-year clinical trial (where
the rst year is the selection period and the second year is the
intervention period), we dened scenarios where the transmission
rate is decreased by 5%, 15%, and 25% during the second year
(Fig. 3a). As a result, the selection year is unaffected by NPIs, and
ab
βRecoveredInfected γ
Lockdown Seasonality
ζ
ζ
Susceptible
Transmission rate
Recovery rate
Immunity loss rate
β
γ
Decrease
Modulation
January
March
May
July
September
November
0
100
200
300
400
500
600
700
Weekly RTI incidence
(per 100,000 patients, all ages)
Lockdown starts
RCGP data
Model
5 years average
2020
Fig. 2 Between-host model based on susceptible, infected, recovered, and again susceptible (SIRS) framework allows to reproduce respiratory tract
infection (RTI) incidence during non-pharmaceutical interventions (NPIs) to mitigate COVID-19 pandemic. a Schematic of implemented SIRS model
where NPI can be modeled by a decrease of the transmission rate. bComparison of model predictions (dashed lines) and data (solid lines) from Royal
College of General Practitioners (RCGP)41 for RTI weekly incidence (per 100,000 all ages) for the 5 years average (green) and 2020 (orange). Lockdown
was started on the 23th of March 2020 in the UK. This date was used to implement the lockdown in the simulations with a decrease of 17.5% of the
transmission rate.
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the intervention period overlaps with the NPIs. We quantied the
effect of NPIs on the efcacy of RTI prophylaxis by running in
silico clinical trials using our mechanistic model applied to the
oral immunomodulator OM-85 in a pediatric population suffering
from recurrent RTIs. For assessing the efcacy of a prophylactic
treatment absolute and relative metrics have been suggested43,44
and therefore, we report model predictions for these metrics
(Fig. 3, Methods): absolute benet (AB, difference between rate of
RTI in both groups, Fig. 3b), event rate ratio (ERR, ratio of RTI
rates between both groups, Fig. 3c), and two-dimensional analysis
of rates of RTIs in treated vs. untreated patients (Effect Model,
Methods: Efcacy analysis, Fig. 3d). We dene here the RTI rate as
the number of RTIs counted during the 12-month follow-up
period (year 2 of the trial): Rt for the treated group and Rc for the
control group. Note that Rt may also refer to the (time-varying)
reproduction number of an epidemic, but we refer to the rate (or
a
bc
de
f
Jan Apr Jul Oct Jan Apr Jul Oct
Time (days)
0
200
400
600
800
Weekly RTI incidence
(per 100,000 patients, all ages)
NPI starts
Selection year Follow-up year
Transmission reduction (%)
0% 5% 15 % 25 %
0 5 15 25
Transmission reduction (%)
0
1
2
3
Absolute benefit
(number of prevented RTIs)
0 5 15 25
Transmission reduction (%)
0.4
0.6
0.8
1.0
RTI rate ratio
(OM-85/Control)
0 1 2 3 4 5 6 7
RC(12-month RTI rate, control)
0
1
2
3
4
5
6
7
RT(12-mont h RTI rate, OM-85)
AB ≥ 1
RR ≥ 0 .2
RC≥3
Transmission
reduction (%)
0
5
15
25
0 5 15 25
Transmission reduction (%)
0
200
400
600
Sample size
0 5 15
Transmission reduction (%)
0
1
2
3
4
Time to recruit (years)
Fig. 3 Results of in silico clinical trials in prophylaxis of respiratory tract infections (RTIs) with four scenarios of non-pharmaceutical interventions
(NPIs) against COVID-19 pandemic with increasing strength (absent - dark purple, mild - light purple, medium - light red, and strong - yellow)
modeled by a decrease of the transmission rate parameter (no reduction, 5%, 15% and 25%, respectively). For all scenarios, the simulations are
run for 2 years. Year 1 is the selection year during which patients are screened and possibly included in an in silico trial. There is no NPI during year 1. The
NPIs are started at the beginning of year 2 as well as the treatment (ten daily administrations of 3.5 mg of OM-85 from the beginning of the month for 3
consecutive months). RTIs are counted for the complete duration of year 2. aWeekly incidence of RTIs per 100,000 is plotted for 2 years of simulations for
the four NPI scenarios. bDistribution (interquartile range, IQR) of absolute benet (number of prevented RTIs in year 2) is plotted for the four NPI
scenarios. Absolute benet can be interpreted as the number of prevented RTIs in year 2 when comparing the treated and the control group. cDistribution
(IQR) of event RTI rate ratio (ERR, treated over control group) is plotted for the four NPI scenarios. dEffect Model plot for the four NPI scenarios. Each in
silico clinical trial is plotted (symbols) with the number of RTIs in the control group as x-coordinate and the number of RTIs in the treated group as y
coordinate. The region of clinically relevant efcacy is indicated in orange. It is dened by at least 1 prevented RTI in absolute benet (dashed-dotted line),
at least 20% reduction in number of RTIs (solid line) and at least 3 RTIs in the control group. eDistribution (IQR) of sample sizes per arm required to show
efcacy of OM-85 treatment in reducing number of RTIs for the four NPI scenarios. fDistribution (IQR) of estimated patient screening times under the four
NPI scenarios by assuming a hypothetical screening rate of 1000 patients per year and by taking year 2 as the selection year (without treatment).
Sensitivity of these results to mechanistic uncertainty is reported in Supplementary Fig. 14.
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risk) of a specic event, here an RTI, in line with earlier work
using the Effect Model methodology45,46. Additionally, we report
an extended range of model predictions obtained with alternative
hypotheses on key mechanisms in Supplementary Fig. 14.
To harmonize the interpretation of different efcacy metrics
(see e.g., Tripepi et al.43), we compared the RTI rates in the
treated group (Rt) vs. RTI rates in the control group (Rc) directly
in a two-dimensional analysis (Effect Model, 3d). Because Rc is
often used to dene the risk for RTI, this analysis characterizes
the efcacy as a function of the risk.
The absolute benet (AB =RcRt) of OM-85 decreases in
parallel to the reduction of the transmission rate: no reduction of
the transmission rate: 1.782.48, 5% reduction: 1.301.86, 15%
reduction: 0.721.06, and 25% reduction: 0.190.38 prevented
RTI episodes. Assuming that an AB of 1 prevented RTI episode
per year would be clinically relevant in a given context (see
Discussion), only NPI-induced transmission rate reduction <15%
fullls this criterion.
The event rate ratio (ERR =Rt/Rc) quanties efcacy based on
event rates in the treated group relative to the control group. It is
a common metric for performing statistical hypothesis testing
with negative binomially distributed count data and may also be
used for sample size estimations. We nd that the ERR does not
vary considerably in all but the strongest NPI scenario (no
reduction of the transmission rate: 0.540.64, 5% reduction:
0.550.67, 15% reduction: 0.550.66 and 25% reduction:
0.580.75, Fig. 3c). In consequence, all analyses based on the
ERR (i.e., sample size estimations or post-hoc power analyses) are
expected to be only affected under strong NPI (e.g., strict
lockdown).
In all scenarios with nonzero NPI-induced viral transmission
rate reduction in year 2, virtual patients experienced fewer RTIs
than in year 1 (5; required by eligibility criterion, no lockdown
in year 1). A transmission rate reduction by 5% showed a
reduction of 1.1 RTIs on average (control group RTI rates are 4.0
vs. 5.1 with 5% reduction, p< 0.001, two-tailed Studentst-test).
Transmission rate reduction by 15% and 25% showed a reduction
of RTI rates of 2.8 and 4.3 RTIs with respect to the non-perturbed
scenario (control group RTI rates are 2.3 and 0.8 vs. 5.1 with 0%
reduction, both p-values are <0.001).
We then re-analyzed the efcacy distributions after the 12-
month follow-up during the perturbed year 2 in relation to
thresholds or for assumed clinical relevance (AB and Rc) and
statistical signicance of the trial (ERR) directly in the xyplane of
Fig. 3d. We indicate a region matching three conditions (orange
area in Fig. 3d, lower right quadrant): (i) recurrent RTIs (RRTIs)
with >3 RTIs per year (in real-life RRTIs are often dened as more
than 3 RTI episodes in the previous year and clinical benetis
considered to prevent the recurrence of RTI) as well as (ii) an
absolute benet of at least 1 RTI per year under which signicant
clinical benet becomes less evident to demonstrate for such
products39 and (iii) a rate reduction of 20% in RTI rate which is a
typical hypothesis for conrmatory trial design efcacy (that can
be demonstrated with reasonable sample size and be clinically
relevant). The percentage of in silico clinical trials complying with
all three criteria is 94.0%, 0.0%, and 0.0% for the mild, medium,
and strong NPI scenarios, respectively compared to 99.0% when
no NPI is applied. We thus regard trials conducted as feasible
when viral transmission rates are reduced by 5% but not >15%,
even though they may still meet their endpoints (given that patient
selection is not impaired in our simulation scenario, Fig. 3a).
Effect of NPI scenario on recruitment. We gauged recruitment
issues for RTI prophylaxis trials with estimations of the sample
size estimated for a hypothesized efcacy in a given at-risk
population (as a function of NPI strength) and needed power
along with a more practical time-to-recruit consideration for
given eligibility criteria (Fig. 3e, f).
The sample size estimations commonly used in RTI prophy-
laxis trial designs are based on ERR, assuming that RTI count
data are negative binomially distributed. We have therefore used
a sample size estimation algorithm (Methods) using the ERR (and
negative binomial dispersion coefcient) obtained from Rc and Rt
distributions in our in silico trials for a signicance level of
α=0.025. Our sample size estimations as a function of NPI
strength closely follow the trend of the ERR itself (no reduction of
the transmission rate: 50, 5% reduction: 56, 15% reduction: 68,
and 25% reduction: 288, Fig. 3e). Except for strong NPI, those
estimates are in line with the unperturbed scenario (NPI does not
affect patient selection in this example). Note that empirical
power at xed sample size of 50 patients per arm also follows this
trend (no reduction of the transmission rate: 0.86, 5% reduction:
0.86, 15% reduction: 0.76, and 25% reduction: 0.34).
We estimated the time required to recruit the estimated sample
sizes (Fig. 3e) if NPIs were started at the beginning of year 1 and
by assuming a constant hypothetical screening rate of 1000
patients per year. Year 1 is the selection year during which
patients are screened and possibly included in an in silico trial.
NPIs introduced during this period could perturb the selection
process. A slight reduction of the transmission rateas small as
5%increases the time to recruit by about 50% from 0.28 years to
0.55 years. The medium and strongest NPI scenarios (15% and
25% transmission rate reduction, respectively) lead to infeasible
recruitment times (3 years and 288 years, respectively).
Discussion
The central aspect of this work is to determine, rationalize and
interpret the possible changes induced by lockdown and other
non-pharmaceutical interventions (NPIs) for pandemic contain-
ment on respiratory disease trials with emphasis on RTI pro-
phylaxis. A clinical trial has two general objectives: rst, to
demonstrate non-zero efcacy of the interventional strategy, a
binary question with a binary answer given by a statistical test;
second, to estimate the size of the clinical benet for benet-risk
assessment. Well-designed trials fulll both objectives through
characterizing the efcacy with a quantitative measure. Not
always, however, are common efcacy measures equally suitable
for statistical testing and estimation of the clinical effect size. In
recurrent RTIs, the event rate ratio (ERR) is often used for sta-
tistical hypothesis testing as this measure applies to negative
binomially distributed count data47,48. Nevertheless, a measured
treatment efcacy that is relative to the control group event rate is
at-risk of incompletely representing the clinical benet in case of
low event rateas in times of NPIs to mitigate the COVID-19
pandemic. We, therefore, ran in silico clinical trials (based on the
SIRS model, the within-host immunological model of RTI in an
individual patient, virtual population, and a simulation protocol
resembling pediatric OM-85 trials) reproducing existing clinical
efcacy data of OM-85 in a pediatric population suffering from
RRTIs. To balance the interpretation for statistical signicance
versus clinical benet considerations of these in silico trials, we
applied different efcacy metrics (AB and ERR) and reconciled
them in a two-dimensional analysis of treated vs. untreated rates
(termed Effect Model, see Methods).
Sample size estimation is of crucial importance for planning
clinical trials. For this, hypotheses on expected efcacy and
chosen statistical power to detect it are needed and these may
have to be adapted to the current pandemic context. Second, it is
important to consider how much the efcacy in a trial can differ
from an efcacy hypothesis used for the planning, especially
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when perturbations arise after the trial has been planned or when
sample size estimates based on historical data need to be used.
Here, the post-hoc power obtained from the statistical analysis of
the trial outcome might be perturbed under NPI. The analysis of
the NPI-dependent efcacy of OM-85 for RTI prophylaxis
revealed that the ERR remains unchanged over a broad range of
NPI scenarios. Because ERR is used for statistical testing and
sample size calculations, both the estimated sample size and the
post-hoc power, are not substantially affected unless strong NPIs,
such as strict lockdown, are applied. In such case, however, the
post-hoc power of trials may be reduced for a given sample size
and consequently trialists should consider an adapted efcacy
scenario for obtaining more realistic estimates.
The situation is different for metrics of the clinical benet,
which assess the benet-risk ratio. Depending on the exact con-
text and affected population, the denition of clinical relevance
may vary. For example, prophylaxis of few LRTI episodes in
neonates (often associated with inception of asthma) will be
clinically relevant compared to prophylaxis of a much higher
number of URTIs needed for clinical relevance in pre-school
children, reecting the different effect on patientslives and/or
long-term consequences. First, children frequently suffer from
RTIs (especially URTIs) and 3 RTI episodes per year can be
considered a normal physiological behavior49. Thus, prevention
of recurrence (>3) of RTIs (of which most are URTIs) appears to
be clinically relevant. however, our analysis has shown that under
medium and strong NPI, the annual control group RTI rate is
already <3, even though it was fullling the denition of recur-
rence in the unperturbed year of patient enrollment. Second,
there might be a threshold for the number of prevented events for
an individual (or at the population scale), which becomes relevant
from a clinical or health economic standpoint. One may assume
that e.g., one prevented URTI could be regarded as relevant, but
we could not identify any guidance on that topic. Here again, we
found that trials under medium and strong NPI scenarios do not
fulll our criterion of AB >1 prevented RTI that could be indi-
cative of a true clinical benet.
The Effect Model methodology45,46, which may be obtained
from meta-analyzing existing clinical data or simulation, is a tool
to rationalize control vs. treated group event rates directly.
Consequently, both clinically meaningful and statistically
demonstrable efcacy can be indicated in one analysis. In the
optimal setting, the metrics used to demonstrate the efcacy with
a statistical test goes hand in hand with the size of the effect
relevant for the benet-risk assessment. This predictivity, how-
ever, seems to be weak under pandemic conditions given the
ascertained dichotomy of NPI on AB and ERR. Under the
medium NPI scenario, a substantial portion of trials with positive
primary endpoint evaluation could be challenged for clinical
relevance of the results and, in fact, clinical benet-related metrics
seem to be the most restrictive criteria when used to assess trial
feasibility a priori. We concluded from this analysis that clinical
studies need to anticipate potentially weak representativity of
traditional or practical endpoints for benet-risk assessment and
that either more relevant endpoints need to be chosen or feasi-
bility studies (including computational studies such as trial
simulation) should be conducted for potential trial design
adjustments.
Our simulation setup for this analysis (year 1: patient selection,
year 2: treatment and follow-up period) reects RTI prophylaxis
trials whose conduction takes place during the current pandemic.
Therefore, we concluded that the benet-risk assessment of these
trials should account for the currently reduced disease burden,
and that supporting data (such as observational studies and
models) should be used to demonstrate that a low number of
prevented episodes under pandemic conditions does not
necessarily mean that under normal conditions equally few epi-
sodes will be prevented.
Recruitment issues are probably the earliest and a very
important indicator for difculties to conduct clinical trials in the
current COVID-19 pandemic era. For respiratory disease trials,
such issues may be notably due to large sample size estimates and
fewer eligible patients. NPI introduced during the follow-up
period, but not during the observation period, merely scales the
number of prevented events in year 2 for an already recruited
population (NPI not present in year 1). Therefore, the included
at-risk population (nor their immunological characteristics) are
not altered in such scenarios as compared to the non-perturbed
one. As the ERR used for statistical efcacy testing is a metric
relative to the rate of events in the control group, it is robust
towards uctuations in the overall disease burden by design.
Therefore, our analysis of estimated sample size for NPI-
corrected efcacy (based on event rate ratios) did not show
considerably increased recruitment needs (Fig. 3e). Assuming
that (e.g., for a trial with a xed budget) an enrollment of 200
eligible patients is feasible, demonstration of efcacy in all but the
strongest (25% reduction) NPI scenario, being introduced at the
beginning of year 2, remains possible with the sample size
planned under no-NPI scenario. We thus conclude that estimated
large sample sizes and the associated issues for recruiting high
numbers of patients are currently not a major difculty for trials
that have started and completed enrollment before 2020.
By considering NPI during the observational period in year 1
of a 2-year trial, we can highlight the collateral effects of COVID-
19 during patient recruitment which are caused by a reduction of
the size of the pool of eligible patients. At-risk populations for a
given age range are included based on their history during a
reference period (e.g., number of RTI episodes during the pre-
ceding 12 months), where the risk for RTI is then dened as the
average number of infections per average number of viral expo-
sures (assumed to be a constant in that time period). In practice,
in trials targeting patients aged 16 years with recurring RTI,
patients with at least 46 RTIs are included while the general
populations suffer from e.g., only 3 episodes on average during
the same time. This way of enriching the population with indi-
viduals at elevated risk, however, depends on the assumption that
the virus exposure is a constant and that consequently the
number of RTIs in the general population is also a constant. A
reduction of the overall disease burden (e.g., by NPI), however,
decreases the number of exposures and average number of RTIs
in general. Consequently, in our simulations, small reductions of
viral transmission already led to a reduced number of virtual
patients who comply with any xed denition of recurrent RTIs.
We could translate this effect into a metric for recruitment dif-
culties, by considering the eligible fraction of the virtual popu-
lation compared to the general virtual population and a dened
xed screening rate (Fig. 3f). Under mild NPIs, recruitment time
already increased by ~50%, which questions the feasibility to
recruit enough patients in timeespecially for trials with a total
planned duration of 612 months. Estimated recruitment times of
3 years for a medium NPI scenario signicantly exceed the 12-
month follow-up time of most trials and can thus be considered
infeasible. As these analyses do not reect any further behavioral
changes and psychological effects (e.g., fear to contract COVID-
19) contributing to barriers to participate in clinical trials, the
presented analysis represents an optimistic scenario. Further, we
did not yet account for year-to-year uctuations in the trans-
mission of respiratory viruses that could add to the perturbation
of NPIs (or cancel it out). Nevertheless, as it is the only scenario
where recruitment time does not exceed a 12-month follow-up,
the mild NPI scenario is probably the only reasonable condition
compatible with recruiting enough patients for RTI prophylaxis
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trials under real-world conditions. Considering that during the
rst UK lockdown, transmission reduction by 17.5% best repro-
duces the disease burden data, 5% reduction as in the mild NPI
scenario is a plausible assumption for a long-term effect on viral
transmission (e.g., masks, a threshold number of people in events,
hand sanitizers in public places). To conclude, the selection of
patients with RRTIs based on pre-pandemic historical data would
only include a very small fraction of patients; thus, we suggest
considering eligibility criteria tailored to the current incidence of
RTIs at a given time to avoid misalignment of targeted and
included population. But then, selecting the right at-risk popu-
lation could become more challenging in turn.
Overall, we present here a mechanistic in silico clinical trial
approach in RTI prophylaxis which can incorporate available
disease burden data to output efcacy metrics relevant for
assessing clinical benets and estimating sample sizes in per-
turbed scenarios (or evaluating impact on the post-hoc power
of a trial for a given sample size) as well as recruitment times
(see summary in the rst two columns of Table 1). Mechanistic
description of the transmission of respiratory viruses can
thereby translate lockdown and social distancing measures into
a decreased rate of RTI events in patients, and into a shift of the
risk-dependent efcacy for OM-85 treatment in clinical trial
simulations. The selected approach has some limitations since
feedback from the patient scale back to the population scale
(e.g., how immunomodulation can reduce viral shedding and
thus transmission) is more challenging to implement. Addi-
tionally, no data are available to calibrate OM-85s effect on
viral shedding or efcacy under lockdown. We made the
assumption that treatment effect and transmission are inde-
pendent factors.
We highlighted that statistical signicance of efcacy may be
less predictive of the clinical benet because there are fewer events
to prevent (due to collateral impact of COVID-19 containment),
and consequently benet-risk assessment based on current RTI-
prophylaxis trials might be difcult to establish. Recruitment of
patients can be impeded as long as intermittent lockdown or
perturbations of seasonal virus transmission persist - in particular
when trialists rely on pre-pandemic historical data for trial design.
Several open questions remain: What are the additional
adjustments required for trial design to account for the effect of
the pandemic? How does the altered and shifted seasonality of
respiratory viruses affect follow-up duration? Is there a potential
benet of using inclusion criteria adapted to pandemic times
(such as incidence matching), and do those adaptations risk to
confound efcacy? What happens if the forecast of the disease
burden turns out to be wrong?
The limitations of traditional clinical trial design methodology
and the proof of concept established in this modeling and
simulation study advocate for an NPI strength-dependent risk
mitigation strategy for which the use of mechanistic computa-
tional models could play a pivotal role. Among measures to
improve clinical signicance such as balancing sample size and
power, adapting statistical methods or adjusting the development
plan (see Table 1for details on suggested measures), modeling
can be used to support go/no-go decisions, optimize trial design,
and additionally serve as digital evidence for a wide range of RTI
prophylaxis-oriented treatments.
Methods
Modeling approach. The in silico clinical trials in this work are simulations
performed with system models using ordinary differential equations (ODEs)
embedded in a virtual population approach where parameters are described by
statistical distributions rather than scalar values, in order to represent different
sources of variability. Each virtual patient corresponds to a vector of parameter
values drawn from the corresponding statistical distribution. Similar to a real
clinical trial protocol, an in silico study protocol denes the use of the model,
virtual population, simulation scenarios, and statistical analyses to answer a
question of interest. It is to note that the model is rather complex thanks to its roots
in systems biology and quantitative systems pharmacology. Despite the deviation
from the principle of parsimony for this study, such type of models can be used for
impactful (e.g., regulatory) decision making when properly validated50.
Multi-scale RTI disease and treatment model. The core element of the com-
putational approach is the coupling of a within-host mechanistic disease model,
representing the viral and immune dynamics, with a between-host disease burden
Table 1 Summary of the effect of NPI on clinical development by NPI strength and recommendations for each scenario. For each
recommendation, a (non-exhaustive) list of specic risk mitigation measures is suggested.
What level of NPI
is expected?
Impact on trial
feasibility
Recommendation
for the trialist
Specic risk mitigation measures
Weak (leading to disease
burden change similar as
year-to-year uctuations)
Assessment of clinical
benet is difcult with
low number of events
Reinforce and underline
clinical signicance of the
demonstrated effect
Select population/endpoints where a smaller (absolute) effect on RTI
prophylaxis is still clinically meaningful (characterized by small minimally
important difference). One example is to focus on prophylaxis of viral infection
induced wheezing or asthma exacerbations, see70,71, rather than upper RTI
(mostly common cold) in the general population
Comprehensive reporting of rates, relative, and absolute benet
Include secondary endpoints that add a diversied and multifaceted view to
the clinical signicance for assessors of the trial results (e.g., symptom-free
days as RTI duration related endpoint)
Seek regulators feedback on the study protocol and statistical analys is plan
with respect to clinical benet assessment
Medium (leading to
substantially lower disease
burden; magnitude of
change with respect to
average exceeds year-to-
year uctuations)
Reduced post-hoc power
with xed sample size and
less available patients that
suffer from xed minimum
number of episodes
Mitigate loss of power
through sample size
adjustment, adaptive trial
design, and statistical
analysis tailored to rare
events
Multi-center trials with access to a larger patient pool can facilitate
recruitment of larger sample sizes under difcult conditions
Use Model Informed Drug Development (MIDD) to leverage the totality of
evidence for an optimal trial design and extrapolation72,73
Primary endpoint analysis based on event rate ratio (ERR) and accounting for
excess zeros, e.g., zero-inated negative binomial regression (ZINB) in frame
of generalized linear models (GLM)74,75
Use trial monitoring and (Bayesian) adaptive trial design76 especially sample
size reestimation (increasing the sample size based on interim data
analysis)77, group sequential designs78 (trials can be stopped early once
signicant results are obtained, or the trial can be stopped for futility)
Seek regulators feedback on any modeling and simulation methods applied
(e.g., FDAs MIDD pilot program)79, for complex innovative trial design and
the statistical analysis (e.g., FDAs complex innovative trial design pilot
program80)
Strong =lockdown
(leading to attenuation
of seasonal epidemic)
High risk of insufcient
sample size and severe
recruitment issues
Change the development
plan
Change development timeline
Conduct observational study to assess the effect of NPI, see e.g., ref. 81
Prioritize retrospective analyses (see ref. 82 for an example in case of OM-85).
Perform exploratory modeling studies
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model, representing the viral dynamics at the population-scale with a Susceptible,
Infectious, Recovered, Susceptible (SIRS) framework, to obtain a multi-scale RTI
and immunomodulation model (Fig. 1). The immunological and the SIRS models
are both ODEs-based deterministic models (equations and parameters provided in
the Supplementary Methods).
Immunological within-host viral infection model. The immunological model,
implementing lytic versus nonlytic immune mechanisms during viral infection, was
designed based on Wodarz et al. 51 to simulate the within-host dynamics in
response to respiratory virus exposure (co-infections are not accounted) (bottom
part of Fig. 1, model equations and parameters are described in Supplementary
Methods, Supplementary Fig. 2, Supplementary Table 1). To translate individual
occurrences of RTI events for a given patient over time into the distributions of
RTI rates in the population, inter- and intra-individual variability need to be taken
into account. For this, stochastic processes determine time points of viral exposure
and current state of antiviral defenses. A patient-specic state of antiviral defenses
(immuno-competence) is therefore distributed in the population and a layer of
random uctuations is added around each individual value. Both distributions were
calibrated so that the RTI distribution in the virtual population represents a
reference RTI prevalence distribution data set (obtained from a reference birth
cohort49). Describing age as a covariate for this distribution required inclusion of a
maturation term into the immune effector functions to reproduce the higher risk
for RTI in young children due the still-developing immune system (Supplementary
Methods, Supplementary Fig. 3).
Between-host viral infection and disease burden model. RTI disease burden was
simulated using a SIRS model (model equations and parameters are described in
Supplementary Methods: Between-host SIRS model) inspired by general literature
on such models52. This SIRS model accounts for the seasonality of infection in an
averaged manner in a given population; it is based on time-dependent transmission
rates of selected viruses reproducing the seasonality of upper and lower RTIs
attributed to respiratory syncytial virus (RSV), rhinovirus (RV), and inuenza
viruses (IV) (Supplementary Fig. 4, Supplementary Table 2). We rst ran the
epidemiological model alone with NPI-adjusted transmission rate (reduction of
mean transmission rates b
0
by 0%, 5%, 15%, 25%) and compared it with data
digitized from the communicable and respiratory disease reports from 2019 to 2020
published in the UK by the Royal College of General Practitioners (RCGP)41. The
outcome of the SIRS model was then used to provide the data for the time-
dependent instantaneous prevalence of RTI for the rest of the model.
Treatment model. To describe the immuno-modulating effect of OM-85 in RTI
prophylaxis, a physiologically based pharmacokinetics and pharmacodynamics
(PBPK/PD) model is linked to the immunological model through ingress in the
respiratory tract of reprogrammed type-1 innate memory like cells53, regulatory
T-cells5456, and polyclonal IgA producing plasma cells57,58 originating from the
intestinal Peyers patches (Supplementary Fig. 5) according to the current under-
standing of OM-85s mechanism of action. Implementation of administration,
distribution, metabolism, and excretion follows common published approaches
(Supplementary Methods: PBPK/PD model of OM-85 effect, model equations are
provided as Supplementary File). In the absence of OM-85 PK data, the unknown
PBPK drug-specic parameters were calibrated using rodent PK data of a similar
product (OM-89)59,60 and were allometrically scaled to human physiology (Sup-
plementary Fig. 6, Supplementary Table 3). Unknown PD-relevant parameters
were calibrated and checked using two sets of human PD response data under
different treatment regimens (Supplementary Figs. 78, Supplementary Table 4).
Calibration of remaining parameters that quantify the size of the efcacy of OM-85
was performed based on the meta-analysis of Yin et al.38 (Supplementary Methods:
Calibration of OM-85 clinical efcacy, Supplementary Figs. 1011).
In silico clinical trial simulations. We simulated placebo-controlled parallel two-
arm trials of RTI prevention with OM-85 in pediatric subjects with 24-month
duration (observational period of 1 year followed by a follow-up period of 1 year
composed of 3 consecutive months of treatment followed by 9 without any
treatment). A virtual population of 104,000 virtual patients was generated. The
entire virtual population was screened during the observational period in the rst
year of the trial. After the rst year, eligibility criteria were evaluated and rando-
mization was performed. In line with the range of annual RTI episodes typically
dening RRTIs (36), children that experienced at least 5 RTIs were included into
the follow-up period. Included virtual subjects were randomly allocated with equal
weight to the interventional and control arms. During the rst 3 months of the
follow-up period, OM-85 was administered every day during the rst 10 con-
secutive days of each month, in line with the currently approved dosing regimen of
OM-85 in the prevention of RTIs. The primary outcome was the number of RTIs
during 1 year follow-up, which was assessed at the end of the trial. 600 in silico
clinical trials were simulated for each of the 4 different NPI scenarios (reduction of
the transmission rate by 0, 5%, 15%, 25%) by randomly sampling 50 subjects per
arm from the screened virtual population (studies meta analyzed by Yin et al.38
have enrolled in average 45.4 patients per arm).
Efcacy analysis. The Effect Model approach45,61 is a tool, which relates the
rates (or risks) of events without treatment (Rc) and with (Rt), as supported by
empirical evidence, simulations, and theoretical considerations6265.While
simulations can be conducted for the same patient in different arms in in silico
trials and yield paired observations, the Effect Model can also be reconciled with
meta-analyses62,66.HerewehaveusedasimilarapproachthatcomparesRTI
rates in a series of individual in silico clinical trials, thus not reporting indivi-
dual, but risk-stratied group metrics. As efcacy metrics, we consider absolute
benet (AB) and the event rate ratio (ERR). Average ERR and AB were assessed
at 1 year follow-up. AB determined from a single in silico trial is the arithmetic
difference between mean RTI rate in the control group (Rc) and in the treatment
group (Rt). ERR refers to the ratio Rt/Rc. Distribution of the ERR and AB per
scenario contain pooled results for different mechanistic conditions and is
visualized as the maximum interquartile range (dened as the difference between
maximum 75th percentile and minimum 25th percentile across mechanistic
conditions). AB and ERR were analyzed with a paired t-test/ANOVA with α
level set at 0.05.
Sample size and recruitment estimation.Samplesizecalculationsforprimary
endpoint analyses of RTI prophylaxis trials require an adapted statistical method
for overdispersed count data. We performed generalized linear regression ana-
lysis with negative binomial distributions (mean and dispersion parameter, glm
function of the R package MASS 7.3-55) for the subsequent use of these para-
meters in the sample size calculation method proposed by Zhu et al. 67.Cal-
culations employed the power.nb.test function of the MKmisc package (1.8)
given the ratio of rates in both trial arms, average dispersion parameter between
both arms (thus varied per NPI scenario; note that xed dispersion parameter
gives similar results, Supplementary Fig. 15), an αof 0.025 and correction for
average study duration (e.g., due to dropout κ=0.75). Based on sample size
calculations and the fraction of the entire virtual population eligible for inclu-
sion, time to recruitment was calculated, assuming that in a typical study in
respiratory diseases, a screening rate of 1000 patients per year can be achieved
per center.
Reporting summary. Further information on research design is available in the Nature
Research Reporting Summary linked to this article.
Data availability
The data (simulation outputs, in silico clinical trials and analyses) generated in this study
and needed to reproduce the results presented in the gures are provided as comma-
separated-value les (csv) compressed into one le (zip) as Supplementary File. 5-year
average and the 20192020 upper and lower RTI (URTI and LRTI) incidence from the
communicable and respiratory disease report 20192020 published in the UK by the
Royal College of General Practitioners (RCGP) can be accessed at the following address:
https://www.rcgp.org.uk/-/media/Files/CIRC/WeeklyReport_Summer_wk31_2020.ashx.
All other datasets used (viral load evolution after experimental viral challenge68, PK/PD
data5860,69, age-dependent RTI distribution49, meta-analyzed clinical efcacy38) were
obtained from published reports whose references are cited. Source data are provided
with this paper.
Code availability
The model source is provided in the Supplementary File as an SBML le (Level 3 Version
2) along with a Python (3.7) script to run the model for a reference patient in a reference
scenario using libroadrunner (http://libroadrunner.org/) a C/C++ library that supports
simulation of SBML based models (version 2.2.0), reference model outputs for all
variables (.csv, .pdf), summary model implementation table (.xls) as well as mapping
between the source and the human-readable description (pdf, xls). For the analysis, the
code (Python 3.9.7, Numpy 1.19.5, Scipy 1.8.0, rpy2 3.4.5, pandas 1.4.1, Matplotlib 3.5.1,
JupyterLab 3.2.9, R 4.0.4, MKmisc 1.8, MASS 7.3-55) used to produce the gures is
provided along with the necessary data as Supplementary File.
Received: 29 October 2021; Accepted: 21 March 2022;
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Acknowledgements
We acknowledge feedback by Jim Bosley and Shiny Martis B. on the manuscript
draft and technical support from Eliott Tixier, Roman Cheplyaka, and Louis Philippe.
Author contributions
A.K., S.A., L.L., C.P., and A.C. supervised the study. S.A., C.C., I.F., N.G., S.G.N., D.S.
developed the model, performed simulations, and analyzed the results. S.A. and A.K.
wrote the manuscript. All authors contributed to the discussion of the results and
reviewed the manuscript.
Competing interests
A.K., S.A., C.C., I.F., N.G., S.G.N., D.S., R.K., B.I., J.P.B. are employees of Novadiscovery.
A.C., L.L., C.P. are employees of OM Pharma. Novadiscovery and OM Pharma funded
the study.
Additional information
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Background: In Italy, the bacterial lysate OM-85 (Broncho-Vaxom®, Broncho-Munal®, Ommunal®, Paxoral®, Vaxoral®) is registered for the prophylaxis of recurrent respiratory tract infections (RTIs) in adults and children above one year of age, but there are limited data on its use in the paediatric population. We aim to estimate the impact of OM-85 treatment on RTIs and antibiotic prescriptions in children. Methods: This study included children aged 1 to 14 years enrolled in Pedianet, a paediatric general practice research database, from January 2007 to June 2017, having at least one prescription of OM-85. Children with less than 12 months of follow-up before (PRE period) and after (POST period) the OM-85 prescription were excluded. The frequency of antibiotic prescriptions and the frequency of RTI episodes in the PRE and POST periods were compared through the post-hoc test. Subgroup analysis was performed in children with recurrent RTIs. Results: 1091 children received 1382 OM-85 prescriptions for a total follow-up of 619,525.5 person-years. Overall, antibiotic prescriptions decreased from a mean of 2.8 (SD (standard deviation) 2.7) prescriptions in the PRE period to a mean of 2.2 (SD 2.6) prescriptions in the POST period (p < 0.0001). RTIs decreased from a mean of 3.4 (SD 2.9) episodes in the PRE period to a mean of 2.5 (SD 2.6) episodes in the POST period (p < 0.0001). No change in antibiotic class was noted, and co-amoxiclav remained the preferred therapy in 28% of cases, followed by amoxicillin. These results were confirmed among children with recurrent RTIs. Conclusions: OM-85 is effective in preventing both antibiotic prescriptions and RTIs in children.
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Background The Center for Drug Evaluation and Research and the Center for Biologics Evaluation and Research of the U.S. Food and Drug Administration have been leaders in advancing science to protect and promote public health by ensuring that safe and effective drugs and biological products are available to those who need them. Recently, new therapeutic discoveries, increased understanding of disease mechanisms, the need for innovation to optimally use resources, and global public health crises have led to an evolving drug development landscape. As a result, the U.S. Food and Drug Administration and medical product developers are faced with unique challenges and opportunities. The U.S. Food and Drug Administration is proactively meeting the challenges of this evolving landscape through various efforts, including the Complex Innovative Trial Design Pilot Meeting Program. Our focus, here, will be on the pilot meeting program. Methods The U.S. Food and Drug Administration has defined a process to facilitate the implementation of the Complex Innovative Trial Design Pilot Meeting Program. The process is transparent and outlines the steps and timeline for submission, review, and meetings. Results Five submitted meeting requests have been selected for participation in the Complex Innovative Trial Design Pilot Meeting Program. Conclusion The pilot meeting program has been successful in further educating stakeholders on the potential uses of complex innovative designs in trials intended to provide substantial evidence of effectiveness. The selected submissions, thus far, have all utilized a Bayesian framework. The reasons for the use of Bayesian approaches may be due to the flexibility provided, the ability to incorporate multiple sources of evidence, and a desire to better understand the U.S. Food and Drug Administration perspective on such approaches. We are confident the pilot meeting program will have continued success and impact the collective goal of bringing safe and effective medical products to patients.
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Background For preventing the spread of the COVID-19 pandemic, measures like wearing mask, social distancing and hand hygiene played crucial roles. These measures may also have affected the expansion of other infectious diseases like respiratory tract infections (RTI) and gastro-intestinal infections (GII). Therefore, we aimed to investigate non-COVID-19 related RTI and GII during the COVID-19 pandemic. Methods Patients with a diagnosis of an acute RTI (different locations) or acute GII documented anonymously in 994 general practitioner (GP) or 192 pediatrician practices in Germany were included. We compared the prevalence of acute RTI and GII between April 2019 - March 2020 and April 2020 - March 2021. Results In GP practices, 715,440 patients were diagnosed with RTI or GII in the non-pandemic period versus 468,753 in the pandemic period; the same trend was observed by pediatricians (275,033 vs. 165,127). By GPs the strongest decrease was observed for the diagnosis of influenza (-71%, p<0.001), followed by acute laryngitis (-64%, p<0.001), acute lower respiratory infections (bronchitis) (-62%, p<0.001), and intestinal infections (-40%, p<0.001). In contrast, the relatively rare viral pneumonia strongly increased by 229% (p<0.001). In pediatrician practices, there was a strong decrease in infection diagnoses, especially influenza (-90%, p<0.001), pneumonia (-73%, p<0.001 viral; -76%, p<0.001 other pneumonias), and acute sinusitis (-66%, p<0.001). No increase was observed for viral pneumonia in children. Conclusion The considerable limitations concerning social life implemented during the COVID-19 pandemic in order to combat the spread of SARS-CoV-2 also resulted in an inadvertent but welcome reduction in other non-Covid-19 respiratory tract and gastro-intestinal infections. This article is protected by copyright. All rights reserved.
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Medical researchers are beginning to shift their focus away from COVID-19 — but the pandemic could continue to affect studies focused on other diseases. Medical researchers are beginning to shift their focus away from COVID-19 — but the pandemic could continue to affect studies focused on other diseases. Credit: Mario Tama/Getty Clinicians care for COVID-19 patients in a makeshift Intensive Care Unit at Harbor-UCLA Medical Center in Torrance, California. Clinicians care for COVID-19 patients in a makeshift Intensive Care Unit at Harbor-UCLA Medical Center in Torrance, California.