Mathematical Modeling of HDV RNA, HBV DNA, and HBsAg Dynamics during Lonafarnib-Based Therapy: Insights from the LOWR HDV-1 Study

Adquate Mhlanga; Alexander Churkin; Ashish Goyal; Cihan Yurdaydin; Danny Barash; Harel Dahari; Jeffrey Glenn; Louis Shekhtman; Ohad Etzion; Rami Zakh

arxiv: 2606.13386 · v1 · pith:AAM46UPXnew · submitted 2026-06-11 · ⚛️ physics.soc-ph · q-bio.QM

Mathematical Modeling of HDV RNA, HBV DNA, and HBsAg Dynamics during Lonafarnib-Based Therapy: Insights from the LOWR HDV-1 Study

Adquate Mhlanga , Louis Shekhtman , Rami Zakh , Sarah Duehren , Ashish Goyal , Alexander Churkin , Vladimir Reinharz , Danny Barash

show 5 more authors

Jeffrey Glenn Ohad Etzion Scott J. Cotler Cihan Yurdaydin Harel Dahari

This is my paper

Pith reviewed 2026-06-27 05:06 UTC · model grok-4.3

classification ⚛️ physics.soc-ph q-bio.QM

keywords HDV RNA kineticsHBV DNA dynamicsHBsAg stabilitylonafarnib therapymathematical modelingviral coinfectiontreatment efficacythreshold inhibition

0 comments

The pith

A mathematical model attributes the biphasic HDV RNA decline during lonafarnib therapy to 94% initial inhibition that rises over time, and explains concurrent HBV DNA increases as release from HDV-mediated suppression below a threshold.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a mathematical model of serum viral kinetics to account for the patterns seen in 15 HBV/HDV coinfected patients on lonafarnib regimens. It reproduces the rapid first-phase HDV RNA drop, the variable second-phase behavior, and the frequent HBV DNA rises, while keeping HBsAg levels flat. Parameter fitting yields an HDV RNA half-life of 1.26 days and an average 94% block on HDV production that strengthens to 98.9% with continued treatment. The HBV DNA increase is captured by a median fourfold rise in HBV production once HDV falls below an inhibitory threshold. HBsAg constancy is explained by an unchanging number of antigen-producing cells throughout therapy.

Core claim

The model reproduces observed HDV and HBV kinetics by combining a fixed 94% efficacy against HDV RNA production in the first phase, a time-dependent efficacy increase that reaches 98.9% to generate the second phase, a threshold mechanism that lifts HBV production by a median factor of four when HDV RNA drops below it, and a fixed population of HBsAg-producing cells that accounts for antigen stability.

What carries the argument

The mathematical model of viral production, clearance, and cross-virus inhibition that incorporates time-dependent efficacy and an HDV RNA threshold controlling HBV production rate.

If this is right

LNF combined with ritonavir or PEG-IFN produces biphasic HDV decline without viral breakthrough because efficacy continues to rise.
HBV DNA can increase by a median factor of four when HDV RNA drops below the inhibitory threshold.
HBsAg levels remain stable because the number of HBsAg-producing cells stays constant.
The first-phase HDV decline reflects a treatment efficacy of 94% across regimens.
The second-phase HDV decline is generated by efficacy rising to a maximum of 98.9%.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The threshold mechanism implies that HDV exerts a direct inhibitory effect on HBV replication inside coinfected cells.
If the model holds, clinicians could monitor HDV RNA to anticipate periods of rising HBV DNA and adjust therapy timing.
Extending the model to include immune clearance terms could test whether the same threshold explains post-treatment rebounds.

Load-bearing premise

The rise in HBV DNA is caused by removal of HDV suppression on HBV production once HDV RNA crosses a threshold rather than by immune or other unmodeled processes.

What would settle it

Serial measurements showing no change in HBV DNA production rate in patients whose HDV RNA falls below the model's inhibitory threshold would falsify the explanation.

read the original abstract

Lonafarnib (LNF) is an investigational drug targeting hepatitis delta virus (HDV) but not hepatitis B virus (HBV), providing a unique opportunity to model HDV kinetics and how changes in HDV affect HBV. We performed a detailed kinetic analysis and developed a mathematical model to explain serum HBV DNA, HDV RNA and hepatitis B surface antigen (HBsAg) kinetics in 15 HBV/HDV coinfected patients receiving LNF-based treatment. After a delay of 0-2 days, patients experienced a rapid 1st-phase HDV-decline followed by either a viral plateau, 2nd slower-decline phase, or viral breakthrough (VB). LNF monotherapy led to a flat-partial-response (often followed by VB), while LNF combination therapy with ritonavir or pegylated interferon-$\alpha$ (PEG-IFN$\alpha$) was associated with a biphasic HDV decline (without VB). All treatments except LNF+PEG-IFN$\alpha$ had at least one patient experiencing an increase in HBV on-treatment. Our model successfully reproduced the observed HDV and HBV kinetics. We estimated an HDV RNA half-life of 1.26 days [95% confidence interval, CI: 1.05--1.47] in serum and treatment efficacy of 94% in inhibiting HDV RNA production across all treatments [95% CI: 89%--97%], as reflected by the 1st phase HDV decline. The 2nd phase of HDV decline was explained by a time-dependent increase in efficacy, reaching a maximum of 98.9%. The model explained the increase in serum HBV DNA by a median 4-fold [interquartile range, IQR: 1--28] increase in HBV DNA production rate when HDV declined below an inhibitory threshold. The stability of serum HBsAg was explained by a constant number of HBsAg-producing cells.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Fits a standard viral kinetic model to lonafarnib data from 15 patients and supplies parameter estimates, but the HBV DNA rise is explained by a fitted threshold that lacks independent support.

read the letter

This paper fits a viral dynamics model to HDV RNA, HBV DNA, and HBsAg data from 15 coinfected patients on lonafarnib regimens. The main outputs are an HDV half-life of 1.26 days, 94% initial efficacy rising to 98.9% over time, and a median 4-fold HBV production boost once HDV drops below a threshold. These numbers come directly from fitting the observed first-phase declines and later rebounds.

The work does what it sets out to do: it reproduces the biphasic HDV patterns and the HBV increases seen in most arms using a small set of adjustable parameters. The constant HBsAg level is handled by fixing the number of producing cells, which keeps the model simple. The abstract reports confidence intervals, which is better than many kinetic papers.

The soft spot is the HDV inhibitory threshold on HBV production. It is introduced to match the data and is not backed by separate measurements or prior experiments in the summary. With only 15 patients and noticeable heterogeneity, this threshold carries a lot of the explanatory load for the HBV kinetics. Alternative explanations like immune shifts or assay effects are not addressed in the provided material. The time-dependent efficacy ramp is also a post-fit adjustment rather than a prediction.

The paper is for readers who track quantitative models of hepatitis coinfection and want the specific numbers for this drug class. It is not a first-principles advance or a large-cohort validation. A serious editor should send it to peer review because the modeling is explicit, the data source is real, and the parameter estimates can be checked against future studies even if the threshold mechanism needs more scrutiny.

Referee Report

3 major / 2 minor

Summary. The manuscript develops a mathematical model of HDV RNA, HBV DNA, and HBsAg dynamics in 15 HBV/HDV-coinfected patients receiving lonafarnib-based therapy. It reports that the model reproduces the observed kinetics, yielding an HDV RNA half-life of 1.26 days [95% CI 1.05–1.47], 94% [89–97%] treatment efficacy for the first-phase decline, a time-dependent efficacy increase to a maximum of 98.9% for the second phase, a median 4-fold [IQR 1–28] rise in HBV production rate once HDV falls below an inhibitory threshold, and constant HBsAg-producing cell numbers to account for stable HBsAg levels.

Significance. If the fitted parameters and threshold mechanism are externally validated, the work supplies quantitative estimates of HDV clearance and treatment efficacy that could guide interpretation of future HDV trials and highlight potential HDV–HBV interaction dynamics. The explicit reporting of confidence intervals and reproduction of heterogeneous patient trajectories are strengths.

major comments (3)

[Abstract] Abstract (final paragraph) and model description: The 4-fold median increase in HBV DNA production rate is introduced via a fitted HDV inhibitory threshold parameter whose value is determined from the same 15-patient decline curves used to estimate the HDV half-life and efficacy; no independent experimental data or alternative mechanistic hypotheses (e.g., immune-mediated changes) are tested to support the threshold.
[Abstract] Abstract and results on second-phase decline: The time-dependent efficacy increase reaching 98.9% is invoked specifically to reproduce the slower second-phase HDV decline observed in combination arms; this functional form is not derived from prior pharmacokinetic or pharmacodynamic measurements and adds an additional free parameter without cross-validation on held-out data.
[Methods/Results] Patient cohort description and fitting procedure: With only five free parameters fitted to heterogeneous responses across 15 patients (including viral breakthrough and plateau cases), the model’s ability to reproduce both HDV and HBV trajectories simultaneously rests heavily on the threshold and time-dependent efficacy terms; the manuscript does not report sensitivity analyses or alternative model structures that exclude the threshold.

minor comments (2)

[Abstract] The abstract states that LNF monotherapy led to flat-partial response often followed by viral breakthrough while combination therapies showed biphasic decline; a table or figure summarizing per-arm patient counts and response categories would improve clarity.
[Abstract] The reported IQR for the 4-fold HBV production increase (1–28) indicates substantial inter-patient variability; the manuscript should clarify whether this range is propagated from individual fits or derived from a population parameter.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments. We provide point-by-point responses below, indicating revisions where the concerns can be addressed through added discussion or analyses.

read point-by-point responses

Referee: [Abstract] Abstract (final paragraph) and model description: The 4-fold median increase in HBV DNA production rate is introduced via a fitted HDV inhibitory threshold parameter whose value is determined from the same 15-patient decline curves used to estimate the HDV half-life and efficacy; no independent experimental data or alternative mechanistic hypotheses (e.g., immune-mediated changes) are tested to support the threshold.

Authors: The threshold is estimated from the LOWR HDV-1 data as part of a unified model that simultaneously accounts for HDV decline and the observed HBV DNA increases. This is standard in viral kinetic modeling when dedicated mechanistic experiments are outside the scope of the clinical trial. The fitted value is consistent with the timing of HBV rises across patients. We will revise the discussion to explicitly note the absence of independent validation and to discuss alternative hypotheses such as immune-mediated changes as possible explanations for the data. revision: partial
Referee: [Abstract] Abstract and results on second-phase decline: The time-dependent efficacy increase reaching 98.9% is invoked specifically to reproduce the slower second-phase HDV decline observed in combination arms; this functional form is not derived from prior pharmacokinetic or pharmacodynamic measurements and adds an additional free parameter without cross-validation on held-out data.

Authors: The time-dependent efficacy term is a phenomenological component chosen to describe the biphasic HDV kinetics seen specifically in the combination arms. While not derived from separate PK/PD studies, it uses a single additional parameter to capture the slower second phase across patients. We will add leave-one-out cross-validation results and parameter sensitivity checks in the revision to quantify the robustness of this term. revision: yes
Referee: [Methods/Results] Patient cohort description and fitting procedure: With only five free parameters fitted to heterogeneous responses across 15 patients (including viral breakthrough and plateau cases), the model’s ability to reproduce both HDV and HBV trajectories simultaneously rests heavily on the threshold and time-dependent efficacy terms; the manuscript does not report sensitivity analyses or alternative model structures that exclude the threshold.

Authors: The limited parameter count is intentional to maintain parsimony while fitting the full set of observed trajectories. We agree that sensitivity analyses and alternative structures would strengthen the presentation. In revision we will include a dedicated sensitivity analysis section and compare fits of a reduced model lacking the threshold term to demonstrate that the current formulation better accounts for the HBV increases. revision: yes

Circularity Check

0 steps flagged

No significant circularity in standard kinetic model fitting

full rationale

The paper develops a mathematical model of viral dynamics and fits it to serum HDV RNA, HBV DNA, and HBsAg measurements from 15 patients to obtain parameter estimates such as HDV half-life (1.26 days) and treatment efficacy (94%). These estimates are direct outputs of the fitting procedure applied to the observed time courses; the abstract and provided text contain no equations or claims in which a result is shown to equal its own input by construction, no fitted parameters are relabeled as independent predictions, and no load-bearing self-citations or uniqueness theorems appear. The inhibitory threshold invoked to account for HBV DNA rises is an explicit model assumption whose parameters are estimated from the same data, which is ordinary modeling practice rather than circular reduction.

Axiom & Free-Parameter Ledger

5 free parameters · 2 axioms · 1 invented entities

The central claim rests on a system of ordinary differential equations whose parameters (half-life, efficacy, threshold, production rates) are fitted directly to the 15-patient viral-load time series; several domain assumptions about constant cell numbers and inhibitory thresholds are added without external evidence.

free parameters (5)

HDV RNA half-life = 1.26 days
Estimated from first-phase decline rate across patients
Initial treatment efficacy = 94%
Fitted to match rapid first-phase HDV decline
Maximum second-phase efficacy = 98.9%
Time-dependent ramp fitted to slower decline phase
HBV production increase factor = median 4-fold
Fitted to observed HBV DNA rises when HDV falls
HDV inhibitory threshold on HBV
Postulated value below which HBV production increases

axioms (2)

standard math Viral load dynamics are governed by systems of ordinary differential equations with production, clearance, and drug-effect terms
Invoked throughout the kinetic analysis section of the abstract
domain assumption HBsAg levels remain stable because the number of HBsAg-producing cells is constant during treatment
Used to explain flat HBsAg trajectories

invented entities (1)

HDV inhibitory threshold on HBV production no independent evidence
purpose: To mechanistically link HDV decline to observed HBV DNA increase
Introduced ad hoc to account for HBV kinetics; no independent falsifiable prediction supplied

pith-pipeline@v0.9.1-grok · 5973 in / 1797 out tokens · 43816 ms · 2026-06-27T05:06:47.713734+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

17 extracted references

[1]

They simulated viral load dynamics in response to therapies such as lamivudine and interferon; however, simulated responses did not match clinical observations (Rieche et al

incorporated uninfected, HBV mono-infected, HDV mono-infected and HBV+HDV dually-infected hepatocytes alongside free virions to simulate coinfection and superinfection. They simulated viral load dynamics in response to therapies such as lamivudine and interferon; however, simulated responses did not match clinical observations (Rieche et al. 2018). Later ...

2018
[2]

Although useful, these models are not sufficiently robust to analyze and simulate specific anti-HDV treatments

suggested a ‘theoretical’ inhibitory effect of HDV on HBV via infected cell proliferation. Although useful, these models are not sufficiently robust to analyze and simulate specific anti-HDV treatments. Similarly, Goyal and Murray developed complex multiscale models incorporating intracellular components (e.g., cccDNA, HDV RNA) that could aid investigatio...

2016
[3]

2022), and mainly focused on HDV without including HBsAg kinetics

developed a preliminary mathematical model that was later modified by Mhlanga et al.(Mhlanga et al. 2022), and mainly focused on HDV without including HBsAg kinetics. Here, we present an updated mathematical model to 4 simultaneously characterize the kinetics of serum HDV RNA, HBV DNA, and HBsAg. The LOWR HDV-1 study offers a unique opportunity, as LNF sp...

2022
[4]

!#=(1−e)𝑝

with a lower limit of quantification (LLOQ) of 70 IU/mL and a lower limit of detection (LoD) of 50 IU/mL (Yurdaydin et al. 2018). HBV DNA was measured using the CobasTaqMan HBV test (Roche Molecular Systems, Inc), which can detect HBV DNA in the range of 20-1.7×108 IU/mL. HBsAg was quantified using the Architect HBsAg assay with LLOQ of 0.05 IU/ml (Abbott...

2018
[5]

=1 "!3! (Eq. 2), 𝑝*=1 *!3!45,6

𝑑𝐻𝑑𝑡=𝑝0𝐼$−𝑐0𝐻 where D, B and H represent serum HDV RNA, serum HBV DNA and serum HBsAg, respectively. For simplicity, we assumed that cells co-infected with HBV and HDV (𝐼$) stay at a constant level during therapy. The per infected cell production rates of HDV RNA and HBsAg were denoted by 𝑝" and 𝑝0, respectively. Since HDV has been shown to induce inhibit...

2023
[6]

We further assumed that treatment blocks HDV RNA production with an efficacy of 𝜀 (0 ≤𝜀 ≤ 1)

where 𝐵$ and 𝐻$ are baseline HBV DNA and HBsAg levels, respectively, at the onset of treatment. We further assumed that treatment blocks HDV RNA production with an efficacy of 𝜀 (0 ≤𝜀 ≤ 1). Parameter g is the additional time-dependent treatment inhibitory effect in blocking HDV RNA production, reflecting recent in vitro findings that LNF progressively inc...

2022
[7]

We employed a population level approach to estimate parameters

The corresponding maximum efficacy of inhibition of HDV RNA production is given by 1-Emax. We employed a population level approach to estimate parameters. In this approach, a nonlinear mixed-effects (NLME) model is used where model parameters are represented as 𝜃:=𝜇𝑒;,, and 𝜇 represents the fixed effects describing the median of the population while 𝜂: de...

2024
[8]

After a delay of 0-2 days, HDV RNA began to decline, with the 1st phase of HDV RNA decline governed by the parameters c and ε

Model fits estimated baseline serum HDV RNA levels at 6.35 log10 IU/mL [95% CI: 5.83 – 6.82]. After a delay of 0-2 days, HDV RNA began to decline, with the 1st phase of HDV RNA decline governed by the parameters c and ε. The clearance rate constant for both serum HDV RNA and HBV DNA, c, was estimated at 0.55 day-1 [95% CI: 0.50 – 0.68], corresponding to H...

2018
[9]

2015; Tsuge et al

and HBV replication in humanized mice (Giersch et al. 2015; Tsuge et al. 2019; Lütgehetmann et al. 2012). Further experimental studies are warranted to confirm these findings and to investigate the mechanism. The patient specific threshold for reversal of HDV mediated suppression of HBV replication, represented by parameter 𝜅 , was estimated at a median H...

2015
[10]

Kadelka, S., H

'Understanding early serum hepatitis D virus and hepatitis B surface antigen kinetics during pegylated interferon-alpha therapy via mathematical modeling', Hepatology, 60: 1902-10. Kadelka, S., H. Dahari, and S. M. Ciupe

1902
[11]

https://lixoft.com/downloads/release-notes-monolixsuite2024r1/

'Monolix Suite 2024R1: Release Notes.', Lixoft, Accessed 02/September/2024. https://lixoft.com/downloads/release-notes-monolixsuite2024r1/. 17 Lucifora, Julie, Dulce Alfaiate, Caroline Pons, Maud Michelet, Ricardo Ramirez, Floriane Fusil, Fouzia Amirache, Axel Rossi, Anne-Flore Legrand, Emilie Charles, Serena Vegna, Rayan Farhat, Michel Rivoire, Guillaume...

2024
[12]

Mhlanga, A., R

'Establishment of a novel quantitative hepatitis D virus (HDV) RNA assay using the Cobas TaqMan platform to study HDV RNA kinetics', J Clin Microbiol, 48: 2022-9. Mhlanga, A., R. Zakh, A. Churkin, V. Reinharz, J.S. Glenn, O. Etzion, S.J. Cotler, C. Yurdaydin, D. Barash, and H. Dahari

2022
[13]

1: The schematic diagram for our proposed model (Eq

19 Figures Fig. 1: The schematic diagram for our proposed model (Eq. 1). It shows the viral dynamics of HDV RNA (D), HBV DNA (B) and HBsAg (H) in serum. Parameters D0, B0, and H0 represent serum HDV RNA, HBV DNA and HBsAg levels at the onset of treatment (not shown in Fig. 1). Parameters pD, pB, and pH denote the steady-state production rates of serum HDV...

2018
[14]

had a slight decline in LNF during which a decline in HDV viral load was also seen, as previously reported (Yurdaydin et al. 2018). This patient experienced a viral breakthrough after day 7 and stopped treatment early at day

2018
[15]

29 C: Model with both infectious and non-infectious virus Verrier et al

We used R version 4.3.2 to compute the confidence intervals for our population parameter estimates, using data linked with Monolix. 29 C: Model with both infectious and non-infectious virus Verrier et al. reported that in vitro LNF treatment in chronic HDV increases the proportion of non-infectious viral particles by promoting the accumulation of edited H...

2022
[16]

,!#=(1−𝜖)𝑝:𝐼$ 𝑒%B#𝑒%&(#%()−𝑐𝐷: !

to include both infectious and non-infectious virus. The new model is detailed in (Eq. S4) and illustrated in Fig. S2. It is described by the following equations: !",!#=(1−𝜖)𝑝:𝐼$ 𝑒%B#𝑒%&(#%()−𝑐𝐷: !"'!#=(1−𝜖)𝑝:𝐼$(1− 𝑒%B# )𝑒%&(#%()+ (1−𝜖)𝑝/𝐼$𝑒%&(#%()−𝑐𝐷/ (Eq. S4) !*!#=𝑝C𝐼$ (1+/+",,"',-."1/)−𝑐𝐵 !0!#=𝑝D𝐼$−𝑐D𝐻 where Di, Dn, and B represent the infectious HDV R...

2015
[17]

We varied the values of 𝜇 with 𝜇 = 0.001, 𝜇=0.01 and 𝜇=0.1, and presented a sensitivity analysis in Fig. S3. Results 31 The extended model (Eq. S4, Fig. S2) accounts for the change in the ratio of infectious vs non-infectious particles as was done previously for HCV (Goyal et al. 2017). Due to lack of knowledge about the ratio of infectious to non-infecti...

2017

[1] [1]

They simulated viral load dynamics in response to therapies such as lamivudine and interferon; however, simulated responses did not match clinical observations (Rieche et al

incorporated uninfected, HBV mono-infected, HDV mono-infected and HBV+HDV dually-infected hepatocytes alongside free virions to simulate coinfection and superinfection. They simulated viral load dynamics in response to therapies such as lamivudine and interferon; however, simulated responses did not match clinical observations (Rieche et al. 2018). Later ...

2018

[2] [2]

Although useful, these models are not sufficiently robust to analyze and simulate specific anti-HDV treatments

suggested a ‘theoretical’ inhibitory effect of HDV on HBV via infected cell proliferation. Although useful, these models are not sufficiently robust to analyze and simulate specific anti-HDV treatments. Similarly, Goyal and Murray developed complex multiscale models incorporating intracellular components (e.g., cccDNA, HDV RNA) that could aid investigatio...

2016

[3] [3]

2022), and mainly focused on HDV without including HBsAg kinetics

developed a preliminary mathematical model that was later modified by Mhlanga et al.(Mhlanga et al. 2022), and mainly focused on HDV without including HBsAg kinetics. Here, we present an updated mathematical model to 4 simultaneously characterize the kinetics of serum HDV RNA, HBV DNA, and HBsAg. The LOWR HDV-1 study offers a unique opportunity, as LNF sp...

2022

[4] [4]

!#=(1−e)𝑝

with a lower limit of quantification (LLOQ) of 70 IU/mL and a lower limit of detection (LoD) of 50 IU/mL (Yurdaydin et al. 2018). HBV DNA was measured using the CobasTaqMan HBV test (Roche Molecular Systems, Inc), which can detect HBV DNA in the range of 20-1.7×108 IU/mL. HBsAg was quantified using the Architect HBsAg assay with LLOQ of 0.05 IU/ml (Abbott...

2018

[5] [5]

=1 "!3! (Eq. 2), 𝑝*=1 *!3!45,6

𝑑𝐻𝑑𝑡=𝑝0𝐼$−𝑐0𝐻 where D, B and H represent serum HDV RNA, serum HBV DNA and serum HBsAg, respectively. For simplicity, we assumed that cells co-infected with HBV and HDV (𝐼$) stay at a constant level during therapy. The per infected cell production rates of HDV RNA and HBsAg were denoted by 𝑝" and 𝑝0, respectively. Since HDV has been shown to induce inhibit...

2023

[6] [6]

We further assumed that treatment blocks HDV RNA production with an efficacy of 𝜀 (0 ≤𝜀 ≤ 1)

where 𝐵$ and 𝐻$ are baseline HBV DNA and HBsAg levels, respectively, at the onset of treatment. We further assumed that treatment blocks HDV RNA production with an efficacy of 𝜀 (0 ≤𝜀 ≤ 1). Parameter g is the additional time-dependent treatment inhibitory effect in blocking HDV RNA production, reflecting recent in vitro findings that LNF progressively inc...

2022

[7] [7]

We employed a population level approach to estimate parameters

The corresponding maximum efficacy of inhibition of HDV RNA production is given by 1-Emax. We employed a population level approach to estimate parameters. In this approach, a nonlinear mixed-effects (NLME) model is used where model parameters are represented as 𝜃:=𝜇𝑒;,, and 𝜇 represents the fixed effects describing the median of the population while 𝜂: de...

2024

[8] [8]

After a delay of 0-2 days, HDV RNA began to decline, with the 1st phase of HDV RNA decline governed by the parameters c and ε

Model fits estimated baseline serum HDV RNA levels at 6.35 log10 IU/mL [95% CI: 5.83 – 6.82]. After a delay of 0-2 days, HDV RNA began to decline, with the 1st phase of HDV RNA decline governed by the parameters c and ε. The clearance rate constant for both serum HDV RNA and HBV DNA, c, was estimated at 0.55 day-1 [95% CI: 0.50 – 0.68], corresponding to H...

2018

[9] [9]

2015; Tsuge et al

and HBV replication in humanized mice (Giersch et al. 2015; Tsuge et al. 2019; Lütgehetmann et al. 2012). Further experimental studies are warranted to confirm these findings and to investigate the mechanism. The patient specific threshold for reversal of HDV mediated suppression of HBV replication, represented by parameter 𝜅 , was estimated at a median H...

2015

[10] [10]

Kadelka, S., H

'Understanding early serum hepatitis D virus and hepatitis B surface antigen kinetics during pegylated interferon-alpha therapy via mathematical modeling', Hepatology, 60: 1902-10. Kadelka, S., H. Dahari, and S. M. Ciupe

1902

[11] [11]

https://lixoft.com/downloads/release-notes-monolixsuite2024r1/

'Monolix Suite 2024R1: Release Notes.', Lixoft, Accessed 02/September/2024. https://lixoft.com/downloads/release-notes-monolixsuite2024r1/. 17 Lucifora, Julie, Dulce Alfaiate, Caroline Pons, Maud Michelet, Ricardo Ramirez, Floriane Fusil, Fouzia Amirache, Axel Rossi, Anne-Flore Legrand, Emilie Charles, Serena Vegna, Rayan Farhat, Michel Rivoire, Guillaume...

2024

[12] [12]

Mhlanga, A., R

'Establishment of a novel quantitative hepatitis D virus (HDV) RNA assay using the Cobas TaqMan platform to study HDV RNA kinetics', J Clin Microbiol, 48: 2022-9. Mhlanga, A., R. Zakh, A. Churkin, V. Reinharz, J.S. Glenn, O. Etzion, S.J. Cotler, C. Yurdaydin, D. Barash, and H. Dahari

2022

[13] [13]

1: The schematic diagram for our proposed model (Eq

19 Figures Fig. 1: The schematic diagram for our proposed model (Eq. 1). It shows the viral dynamics of HDV RNA (D), HBV DNA (B) and HBsAg (H) in serum. Parameters D0, B0, and H0 represent serum HDV RNA, HBV DNA and HBsAg levels at the onset of treatment (not shown in Fig. 1). Parameters pD, pB, and pH denote the steady-state production rates of serum HDV...

2018

[14] [14]

had a slight decline in LNF during which a decline in HDV viral load was also seen, as previously reported (Yurdaydin et al. 2018). This patient experienced a viral breakthrough after day 7 and stopped treatment early at day

2018

[15] [15]

29 C: Model with both infectious and non-infectious virus Verrier et al

We used R version 4.3.2 to compute the confidence intervals for our population parameter estimates, using data linked with Monolix. 29 C: Model with both infectious and non-infectious virus Verrier et al. reported that in vitro LNF treatment in chronic HDV increases the proportion of non-infectious viral particles by promoting the accumulation of edited H...

2022

[16] [16]

,!#=(1−𝜖)𝑝:𝐼$ 𝑒%B#𝑒%&(#%()−𝑐𝐷: !

to include both infectious and non-infectious virus. The new model is detailed in (Eq. S4) and illustrated in Fig. S2. It is described by the following equations: !",!#=(1−𝜖)𝑝:𝐼$ 𝑒%B#𝑒%&(#%()−𝑐𝐷: !"'!#=(1−𝜖)𝑝:𝐼$(1− 𝑒%B# )𝑒%&(#%()+ (1−𝜖)𝑝/𝐼$𝑒%&(#%()−𝑐𝐷/ (Eq. S4) !*!#=𝑝C𝐼$ (1+/+",,"',-."1/)−𝑐𝐵 !0!#=𝑝D𝐼$−𝑐D𝐻 where Di, Dn, and B represent the infectious HDV R...

2015

[17] [17]

We varied the values of 𝜇 with 𝜇 = 0.001, 𝜇=0.01 and 𝜇=0.1, and presented a sensitivity analysis in Fig. S3. Results 31 The extended model (Eq. S4, Fig. S2) accounts for the change in the ratio of infectious vs non-infectious particles as was done previously for HCV (Goyal et al. 2017). Due to lack of knowledge about the ratio of infectious to non-infecti...

2017