A phase 3, double-blind, parallel-group study to evaluate the efficacy and safety of tezacaftor in combination with ivacaftor in participants 6 through 11 years of age with cystic fibrosis homozygous for F508del or heterozygous for the F508del-CFTR mutation and a residual function mutation.

BACKGROUND: The CFTR modulator tezacaftor/ivacaftor was efficacious and generally safe and well tolerated in Phase 3 studies in participants ≥12 years of age with cystic fibrosis (CF) homozygous for the F508del-CFTR mutation or heterozygous with a residual function-CFTR mutation (F/F or F/RF respectively). We evaluated tezacaftor/ivacaftor's efficacy and safety over 8 weeks in participants 6 through 11 years of age with these mutations. METHODS: Participants were randomized 4:1 to tezacaftor/ivacaftor or a blinding group (placebo for F/F, ivacaftor for F/RF). The primary endpoint was within-group change from baseline in the lung clearance index 2·5 (LCI2·5) through Week 8. Secondary endpoints were change from baseline in sweat chloride (SwCl), cystic fibrosis questionnaire-revised (CFQ-R) respiratory domain score, and safety. RESULTS: Sixty-seven participants received at least one study drug dose. Of those, 54 received tezacaftor/ivacaftor (F/F, 42; F/RF, 12), 10 placebo, and 3 ivacaftor; 66 completed the study. The within-group change in LCI2·5 was significantly reduced (improved) by -0·51 (95% CI: -0·74, -0·29). SwCl concentration decreased (improved) by -12·3 mmol/L and CFQ-R respiratory domain score increased (improved, nonsignificantly) by 2·3 points. There were no serious adverse events (AEs) or AEs leading to tezacaftor/ivacaftor discontinuation or interruption. The most common AEs (≥10%) in participants receiving tezacaftor/ivacaftor were cough, headache, and productive cough. CONCLUSIONS: Tezacaftor/ivacaftor improved lung function (assessed using LCI) and CFTR function (measured by SwCl concentration) in participants 6 through 11 years of age with F/F or F/RF genotypes. Tezacaftor/ivacaftor was safe and well tolerated; no new safety concerns were identified.

Ivacaftor in Infants Aged 4 to <12 Months with Cystic Fibrosis and a Gating Mutation. Results of a Two-Part Phase 3 Clinical Trial.

Rationale: We previously reported that ivacaftor was safe and well tolerated in cohorts aged 12 to <24 months with cystic fibrosis and gating mutations in the ARRIVAL study; here, we report results for cohorts aged 4 to <12 months.Objectives: To evaluate the safety, pharmacokinetics, and pharmacodynamics of ivacaftor in infants aged 4 to <12 months with one or more gating mutations.Methods: ARRIVAL is a single-arm phase 3 study. Infants received 25 mg or 50 mg ivacaftor every 12 hours on the basis of age and weight for 4 days in part A and 24 weeks in part B.Measurements and Main Results: Primary endpoints were safety (parts A and B) and pharmacokinetics (part A). Secondary/tertiary endpoints (part B) included pharmacokinetics and changes in sweat chloride levels, growth, and markers of pancreatic function. Twenty-five infants received ivacaftor, 12 in part A and 17 in part B (four infants participated in both parts). Pharmacokinetics was consistent with that in older groups. Most adverse events were mild or moderate. In part B, cough was the most common adverse event (n = 10 [58.8%]). Five infants (part A, n = 1 [8.3%]; part B, n = 4 [23.5%]) had serious adverse events, all of which were considered to be not or unlikely related to ivacaftor. No deaths or treatment discontinuations occurred. One infant (5.9%) experienced an alanine transaminase elevation >3 to ≤5× the upper limit of normal at Week 24. No other adverse trends in laboratory tests, vital signs, or ECG parameters were reported. Sweat chloride concentrations and measures of pancreatic obstruction improved.Conclusions: This study of ivacaftor in the first year of life supports treating the underlying cause of cystic fibrosis in children aged ≥4 months with one or more gating mutations.Clinical trial registered with clinicaltrials.gov (NCT02725567).

Correction to: Physiologically Based Pharmacokinetic Modeling of CFTR Modulation in People with Cystic Fibrosis Transitioning from Mono or Dual Regimens to Triple-Combination Elexacaftor/Tezacaftor/Ivacaftor.

The original version of this article unfortunately contained a mistake.

Physiologically Based Pharmacokinetic Modeling of CFTR Modulation in People with Cystic Fibrosis Transitioning from Mono or Dual Regimens to Triple-Combination Elexacaftor/Tezacaftor/Ivacaftor.

INTRODUCTION: The triple-combination (TC) cystic fibrosis transmembrane conductance regulator (CFTR) modulator regimen elexacaftor, tezacaftor, and ivacaftor was shown to be safe and efficacious in phase 3 trials of people with cystic fibrosis (pwCF) ≥ 12 years of age with ≥ 1 F508del-CFTR allele. Here, a simulation study predicted ivacaftor, tezacaftor, and elexacaftor exposures and impacts on CFTR modulation following transition from ivacaftor [a cytochrome P450 3A (CYP3A) substrate], lumacaftor (a CYP3A inducer)/ivacaftor, or tezacaftor/ivacaftor to TC. METHODS: Physiologically based pharmacokinetic (PBPK) modeling was used to evaluate plasma exposures during transition from mono- or dual-combination CFTR modulator regimens to TC. PBPK models were parameterized using data from human hepatocytes to account for CYP3A induction by lumacaftor and validated to match clinical data from healthy volunteers and pwCF. Using dosing regimens for pwCF ≥ 12 years of age, simulations were performed for ivacaftor, lumacaftor/ivacaftor, and tezacaftor/ivacaftor dosing for 14 days followed by immediate transition to elexacaftor/tezacaftor/ivacaftor dosing for 14 days. Drug exposures during transitions were compared with respective half-maximal effective concentrations (EC50) estimated from efficacy endpoint data from clinical studies. RESULTS: In simulations of immediate transition from ivacaftor or tezacaftor/ivacaftor to TC, the preceding treatment had no impact on ivacaftor, tezacaftor, or elexacaftor exposures. In simulations of immediate transition from lumacaftor/ivacaftor to TC, ivacaftor exposure decreased to 64% of maximum effective concentration (EC), due to reduction in ivacaftor dose and residual CYP3A4 induction, then returned to 90-95% of maximum EC. Lumacaftor-mediated CYP3A induction resolved within approximately 2 weeks. In all simulations, ivacaftor, tezacaftor, and elexacaftor exposures approached steady state within 2 weeks following transition and, at all times, ivacaftor and ≥ 1 CFTR corrector remained above EC50. CONCLUSION: PBPK modeling indicates that immediate transition to the elexacaftor/tezacaftor/ivacaftor regimen from an ivacaftor, lumacaftor/ivacaftor, or tezacaftor/ivacaftor regimen results in sustained CFTR modulation in pwCF ≥ 12 years of age.

A phase 3 study of tezacaftor in combination with ivacaftor in children aged 6 through 11 years with cystic fibrosis.

BACKGROUND: Tezacaftor/ivacaftor is a new treatment option in many regions for patients aged ≥12 years who are homozygous (F/F) or heterozygous for the F508del-CFTR mutation and a residual function (F/RF) mutation. This Phase 3, 2-part, open-label study evaluated the pharmacokinetics (PK), safety, tolerability, and efficacy of tezacaftor/ivacaftor in children aged 6 through 11 years with these mutations. METHODS: Part A informed weight-based tezacaftor/ivacaftor dosages for part B. The primary objective of part B was to evaluate the safety and tolerability of tezacaftor/ivacaftor through 24 weeks; the secondary objective was to evaluate efficacy based on changes from baseline in percentage predicted forced expiratory volume in 1 s (ppFEV1), growth parameters, sweat chloride, and the Cystic Fibrosis Questionnaire-Revised (CFQ-R) respiratory domain score. RESULTS: After PK analysis in part A, 70 children received ≥1 dose of tezacaftor/ivacaftor in part B; 67 children completed treatment. Exposures in children aged 6 through 11 years were within the target range for those observed in patients aged ≥12 years. The safety profile of tezacaftor/ivacaftor was generally similar to prior studies in patients aged ≥12 years. One child discontinued treatment for a serious adverse event of constipation. Tezacaftor/ivacaftor treatment improved sweat chloride levels and CFQ-R respiratory domain scores, mean ppFEV1 remained stable in the normal range, and growth parameters remained stable over 24 weeks. CONCLUSIONS: Tezacaftor/ivacaftor was generally safe and well tolerated, and improved CFTR function in children aged 6 through 11 years with CF with F/F and F/RF genotypes, supporting tezacaftor/ivacaftor use in this age group. NCT02953314.

Modeling population heterogeneity in viral dynamics for chronic hepatitis C infection: Insights from Phase 3 telaprevir clinical studies.

Viral dynamic modelling has proven useful for designing clinical studies and predicting treatment outcomes for patients infected with the hepatitis C virus. Generally these models aim to capture and predict the on-treatment viral load dynamics from a small study of individual patients. Here, we explored extending these models (1) to clinical studies with numerous patients and (2) by incorporating additional data types, including sequence data and prior response to interferon. Data from Phase 3 clinical studies of the direct-acting antiviral telaprevir (T; total daily dose of 2250 mg) combined with pegylated-interferon alfa and ribavirin (PR) were used for the analysis. The following data in the treatment-naïve population were reserved to verify the model: (1) a T/PR regimen where T was dosed every 8 h for 8 weeks (T8(q8h)/PR) and (2) a T/PR regimen where T was dosed twice daily for 12 weeks (T12(b.i.d.)/PR). The resulting model accurately predicted (1) sustained virologic response rates for both of these dosing regimens and (2) viral breakthrough characteristics of the T8(q8h)/PR regimen. Since the observed viral variants depend on the T exposure, the second verification suggested that the model was correctly sensitive to the different T regimen even though the model was developed using data from another T regimen. Furthermore, the model predicted that b.i.d. T dosing was comparable to q8h T dosing in the PR-experienced population, a comparison that has not been made in a controlled clinical study. The methods developed in this work to estimate the variability occurring below the limit of detection for the viral load were critical for making accurate predictions.

A signature for immune response correlates with HCV treatment outcome in Caucasian subjects.

This data article contains Supplementary material for a published research article describing a whole-blood proteomic signature that predicts treatment outcome for subjects infected with hepatitis C virus (HCV) [1]. The proteomic signature is derived from whole-blood samples from subjects infected with HCV. The article includes detailed experimental and computational methods used in the analysis. The article also includes tables of demographic and other information about the subjects. Finally, the article includes several figures and tables showing detailed results of the analyses (e.g. lists of identified proteins and coefficients/ROC curves for the regression models).

A signature for immune response correlates with HCV treatment outcome in Caucasian subjects.

Broad proteomic profiling was performed on serum samples of phase 2 studies (PROVE1, PROVE2, and PROVE3) of the direct-acting antiviral drug telaprevir in combination with peg-interferon and ribavirin in subjects with HCV. Using only profiling data from subjects treated with peg-interferon and ribavirin, a signature composed of pretreatment levels of 13 components was identified that correlated well (R(2)=0.68) with subjects' underlying immune response as measured by week 4 viral decline and was highly predictive of sustained virologic response in non-African American subjects (AUC=0.99). The signature was validated by predicting in an independent cohort of non-African American subjects treated with telaprevir, peg-interferon and ribavirin (AUC=0.854). Samples from extreme responders were over-represented in these analyses. Proteins identified as differentially-expressed between responders and non-responders to HCV treatment were quantified using multiple reaction monitoring in samples from all Caucasian subjects in the peg-interferon and ribavirin arms of PROVE1 and PROVE2, revealing 15 proteins that were significantly differentially expressed between treatment responders and non-responders. Seven of the proteins are part of focal adhesions or other macromolecular assemblies that form structural links between integrins and the actin cytoskeleton and are involved in antiviral response. BIOLOGICAL SIGNIFICANCE: HCV is a significant health problem. We describe a novel approach for identifying markers that predicts HCV treatment response different treatment regimens and use this approach to identify a novel HCV treatment response signature. The signature has potential to guide optimization of HCV treatment regimens.

Modeling viral evolutionary dynamics after telaprevir-based treatment.

For patients infected with hepatitis C virus (HCV), the combination of the direct-acting antiviral agent telaprevir, pegylated-interferon alfa (Peg-IFN), and ribavirin (RBV) significantly increases the chances of sustained virologic response (SVR) over treatment with Peg-IFN and RBV alone. If patients do not achieve SVR with telaprevir-based treatment, their viral population is often significantly enriched with telaprevir-resistant variants at the end of treatment. We sought to quantify the evolutionary dynamics of these post-treatment resistant variant populations. Previous estimates of these dynamics were limited by analyzing only population sequence data (20% sensitivity, qualitative resistance information) from 388 patients enrolled in Phase 3 clinical studies. Here we add clonal sequence analysis (5% sensitivity, quantitative) for a subset of these patients. We developed a computational model which integrates both the qualitative and quantitative sequence data, and which forms a framework for future analyses of drug resistance. The model was qualified by showing that deep-sequence data (1% sensitivity) from a subset of these patients are consistent with model predictions. When determining the median time for viral populations to revert to 20% resistance in these patients, the model predicts 8.3 (95% CI: 7.6, 8.4) months versus 10.7 (9.9, 12.8) months estimated using solely population sequence data for genotype 1a, and 1.0 (0.0, 1.4) months versus 0.9 (0.0, 2.7) months for genotype 1b. For each individual patient, the time to revert to 20% resistance predicted by the model was typically comparable to or faster than that estimated using solely population sequence data. Furthermore, the model predicts a median of 11.0 and 2.1 months after treatment failure for viral populations to revert to 99% wild-type in patients with HCV genotypes 1a or 1b, respectively. Our modeling approach provides a framework for projecting accurate, quantitative assessment of HCV resistance dynamics from a data set consisting of largely qualitative information.

The stochastic quasi-steady-state assumption: reducing the model but not the noise.

Highly reactive species at small copy numbers play an important role in many biological reaction networks. We have described previously how these species can be removed from reaction networks using stochastic quasi-steady-state singular perturbation analysis (sQSPA). In this paper we apply sQSPA to three published biological models: the pap operon regulation, a biochemical oscillator, and an intracellular viral infection. These examples demonstrate three different potential benefits of sQSPA. First, rare state probabilities can be accurately estimated from simulation. Second, the method typically results in fewer and better scaled parameters that can be more readily estimated from experiments. Finally, the simulation time can be significantly reduced without sacrificing the accuracy of the solution.

Implications of decoupling the intracellular and extracellular levels in multi-level models of virus growth.

Virus infections are characterized by two distinct levels of detail: the intracellular level describing how viruses hijack the host machinery to replicate, and the extracellular level describing how populations of virus and host cells interact. Deterministic, population balance models for viral infections permit incorporation of both the intracellular and extracellular levels of information. In this work, we identify assumptions that lead to exact, selective decoupling of the interaction between the intracellular and extracellular levels, effectively permitting solution of first the intracellular level, and subsequently the extracellular level. This decoupling leads to (1) intracellular and extracellular models of viral infections that have been previously reported and (2) a significant reduction in the computational expense required to solve the model. However, the decoupling restricts the behaviors that can be modeled. Simulation of a previously reported multi-level model demonstrates this decomposition when the intracellular level of description consists of numerous reaction events. Additionally, examples demonstrate that viruses can persist even when the intracellular level of description cannot sustain a steady-state production of virus (i.e., has only a trivial equilibrium). We expect the combination of this modeling framework with experimental data to result in a quantitative, systems-level understanding of viral infections and cellular antiviral strategies that will facilitate controlling both these infections and antiviral strategies.

Image-guided modeling of virus growth and spread.

Although many tools of cellular and molecular biology have been used to characterize single intracellular cycles of virus growth, few culture methods exist to study the dynamics of spatially spreading viruses over multiple generations. We have previously developed a method that addresses this need by tracking the spread of focal infections using immunocytochemical labeling and digital imaging. Here, we build reaction-diffusion models to account for spatio-temporal patterns formed by the spreading viral infection front as well as data from a single cycle of virus growth (one-step growth). Systems with and without the interferon-mediated antiviral response of the host cells are considered. Dynamic images of the spreading infections guide iterative model refinement steps that lead to reproduction of all of the salient features contained in the images, not just the velocity of the infection front. The optimal fits provide estimates for key parameters such as virus-host binding and the production rate of interferon. For the examined data, highly-lumped infection models that ignore the one-step growth dynamics provide a comparable fit to models that more accurately account for these dynamics, highlighting the fact that increased model complexity does not necessarily translate to improved fit. This work demonstrates how model building can facilitate the interpretation of experiments by highlighting contributions from both biological and methodological factors.

Implications of rewiring bacterial quorum sensing.

Bacteria employ quorum sensing, a form of cell-cell communication, to sense changes in population density and regulate gene expression accordingly. This work investigated the rewiring of one quorum-sensing module, the lux circuit from the marine bacterium Vibrio fischeri. Steady-state experiments demonstrate that rewiring the network architecture of this module can yield graded, threshold, and bistable gene expression as predicted by a mathematical model. The experiments also show that the native lux operon is most consistent with a threshold, as opposed to a bistable, response. Each of the rewired networks yielded functional population sensors at biologically relevant conditions, suggesting that this operon is particularly robust. These findings (i) permit prediction of the behaviors of quorum-sensing operons in bacterial pathogens and (ii) facilitate forward engineering of synthetic gene circuits.

Two classes of quasi-steady-state model reductions for stochastic kinetics.

The quasi-steady-state approximation (QSSA) is a model reduction technique used to remove highly reactive species from deterministic models of reaction mechanisms. In many reaction networks the highly reactive intermediates (QSSA species) have populations small enough to require a stochastic representation. In this work we apply singular perturbation analysis to remove the QSSA species from the chemical master equation for two classes of problems. The first class occurs in reaction networks where all the species have small populations and the QSSA species sample zero the majority of the time. The perturbation analysis provides a reduced master equation in which the highly reactive species can sample only zero, and are effectively removed from the model. The reduced master equation can be sampled with the Gillespie algorithm. This first stochastic QSSA reduction is applied to several example reaction mechanisms (including Michaelis-Menten kinetics) [Biochem. Z. 49, 333 (1913)]. A general framework for applying the first QSSA reduction technique to new reaction mechanisms is derived. The second class of QSSA model reductions is derived for reaction networks where non-QSSA species have large populations and QSSA species numbers are small and stochastic. We derive this second QSSA reduction from a combination of singular perturbation analysis and the Omega expansion. In some cases the reduced mechanisms and reaction rates from these two stochastic QSSA models and the classical deterministic QSSA reduction are equivalent; however, this is not usually the case.

Synthetic gene circuits: design with directed evolution.

Synthetic circuits offer great promise for generating insights into nature's underlying design principles or forward engineering novel biotechnology applications. However, construction of these circuits is not straightforward. Synthetic circuits generally consist of components optimized to function in their natural context, not in the context of the synthetic circuit. Combining mathematical modeling with directed evolution offers one promising means for addressing this problem. Modeling identifies mutational targets and limits the evolutionary search space for directed evolution, which alters circuit performance without the need for detailed biophysical information. This review examines strategies for integrating modeling and directed evolution and discusses the utility and limitations of available methods.

Stochastic simulation of catalytic surface reactions in the fast diffusion limit.

The master equation of a lattice gas reaction tracks the probability of visiting all spatial configurations. The large number of unique spatial configurations on a lattice renders master equation simulations infeasible for even small lattices. In this work, a reduced master equation is derived for the probability distribution of the coverages in the infinite diffusion limit. This derivation justifies the widely used assumption that the adlayer is in equilibrium for the current coverages and temperature when all reactants are highly mobile. Given the reduced master equation, two novel and efficient simulation methods of lattice gas reactions in the infinite diffusion limit are derived. The first method involves solving the reduced master equation directly for small lattices, which is intractable in configuration space. The second method involves reducing the master equation further in the large lattice limit to a set of differential equations that tracks only the species coverages. Solution of the reduced master equation and differential equations requires information that can be obtained through short, diffusion-only kinetic Monte Carlo simulation runs at each coverage. These simulations need to be run only once because the data can be stored and used for simulations with any set of kinetic parameters, gas-phase concentrations, and initial conditions. An idealized CO oxidation reaction mechanism with strong lateral interactions is used as an example system for demonstrating the reduced master equation and deterministic simulation techniques.

On the origins of approximations for stochastic chemical kinetics.

This paper considers the derivation of approximations for stochastic chemical kinetics governed by the discrete master equation. Here, the concepts of (1) partitioning on the basis of fast and slow reactions as opposed to fast and slow species and (2) conditional probability densities are used to derive approximate, partitioned master equations, which are Markovian in nature, from the original master equation. Under different conditions dictated by relaxation time arguments, such approximations give rise to both the equilibrium and hybrid (deterministic or Langevin equations coupled with discrete stochastic simulation) approximations previously reported. In addition, the derivation points out several weaknesses in previous justifications of both the hybrid and equilibrium systems and demonstrates the connection between the original and approximate master equations. Two simple examples illustrate situations in which these two approximate methods are applicable and demonstrate the two methods' efficiencies.