A scalable approach for continuous time Markov models with covariates.

Existing methods for fitting continuous time Markov models (CTMM) in the presence of covariates suffer from scalability issues due to high computational cost of matrix exponentials calculated for each observation. In this article, we propose an optimization technique for CTMM which uses a stochastic gradient descent algorithm combined with differentiation of the matrix exponential using a Padé approximation. This approach makes fitting large scale data feasible. We present two methods for computing standard errors, one novel approach using the Padé expansion and the other using power series expansion of the matrix exponential. Through simulations, we find improved performance relative to existing CTMM methods, and we demonstrate the method on the large-scale multiple sclerosis NO.MS data set.

The Development of Ofatumumab, a Fully Human Anti-CD20 Monoclonal Antibody for Practical Use in Relapsing Multiple Sclerosis Treatment.

The importance of B cells in multiple sclerosis (MS) has been demonstrated through the advent of B-cell-depleting anti-CD20 antibody therapies. Ofatumumab is the first fully human anti-CD20 monoclonal antibody (mAb) developed and tested for subcutaneous (SC) self-administration at monthly doses of 20 mg, and has been approved in the US, UK, EU, and other regions and countries worldwide for the treatment of relapsing MS. The development goal of ofatumumab was to obtain a highly efficacious anti-CD20 therapy, with a safety and tolerability profile that allows for self-administration by MS patients at home and a positive benefit-risk balance for use in the broad relapsing MS population. This development goal was enabled by the unique binding site, higher affinity to B cells, and higher potency of ofatumumab compared to previous anti-CD20 mAbs; these properties of ofatumumab facilitate rapid B-cell depletion and maintenance with a low dose at a low injection volume (20 mg/0.4 ml). The high potency in turn enables the selective targeting of B cells that reside in the lymphatic system via subcutaneous (SC) administration. Through a comprehensive dose-finding program in two phase 2 studies (one intravenous and one SC) and model simulations, it was found that safety and tolerability can be further improved, and the risk of systemic injection-related reactions (IRRs) minimized, by avoiding doses ≥ 30 mg, and by reaching initial and rapid B-cell depletion via stepwise weekly administration of ofatumumab at Weeks 0, 1, and 2 (instead of a single high dose). Once near-complete B-cell depletion is reached, it can be maintained by monthly doses of 20 mg/0.4 ml. Indeed, in phase 3 trials (ASCLEPIOS I/II), rapid and sustained near-complete B-cell depletion (largely independent of body weight, race and other factors) was observed with this dosing regimen, which resulted in superior efficacy of ofatumumab versus teriflunomide on relapse rates, disability worsening, neuronal injury (serum neurofilament light chain), and imaging outcomes. Likely due to its fully human nature, ofatumumab has a low immunogenic risk profile-only 2 of 914 patients receiving ofatumumab in ASCLEPIOS I/II developed anti-drug antibodies-and this may also underlie the infrequent IRRs (20% with ofatumumab vs. 15% with the placebo injection in the teriflunomide arm) that were mostly (99.8%) mild to moderate in severity. The overall rates of infections and serious infections in patients treated with ofatumumab were similar to those in patients treated with teriflunomide (51.6% vs. 52.7% and 2.5% vs. 1.8%, respectively). The benefit-risk profile of ofatumumab was favorable compared to teriflunomide in the broad RMS population, and also in the predefined subgroups of both recently diagnosed and/or treatment-naïve patients, as well as previously disease-modifying therapy-treated patients. Interim data from the ongoing extension study (ALITHIOS) have shown that long-term treatment with ofatumumab up to 4 years is well-tolerated in RMS patients, with no new safety risks identified. In parallel to the phase 3 trials in which SC administration was carried out with a pre-filled syringe, an autoinjector pen for more convenient self-administration of the ofatumumab 20 mg dose was developed and is available for use in clinical practice.

Rationale for Use of Sphingosine-1-Phosphate Receptor Modulators in COVID-19 Patients: Overview of Scientific Evidence.

Maladjusted immune responses to the coronavirus disease 2019 (COVID-19), for example, cytokine release syndrome, may result in immunopathology and acute respiratory distress syndrome. Sphingosine-1-phosphate (S1P), a bioactive lipid mediator, and its S1P receptor (S1PR) are crucial in maintaining endothelial cell chemotaxis and barrier integrity. Apart from the S1P1 receptor-mediated mechanisms of sequestration of cytotoxic lymphocytes, including Th-17 and S1P1/2/3-mediated endothelial barrier functions, S1PR modulators may also attenuate cytokine release via activation of serine/threonine protein phosphatase 2A and enhance the pulmonary endothelial barrier via the c-Abl tyrosine kinase pathway. Chronic treatment with fingolimod (S1PR1,3,4,5 modulator) and siponimod (S1PR1,5 modulator) has demonstrated efficacy in reducing inflammatory disease activity and slowing down disease progression in multiple sclerosis. The decision to selectively suppress the immunity of a critically ill patient with COVID-19 remains a difficult choice. It has been suggested that treatment with fingolimod or siponimod may be appropriate to attenuate severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-induced hyperinflammation in patients with COVID-19 since these patients are already monitored in an intensive care setting. Here, we review the use of S1PR modulators, fingolimod and siponimod, in regulating the inflammatory response to SARS-CoV-2 with the aim of understanding their potential rationale use in patients with COVID-19.

Population Pharmacokinetic-B Cell Modeling for Ofatumumab in Patients with Relapsing Multiple Sclerosis.

BACKGROUND: Ofatumumab, a fully human anti-CD20 monoclonal antibody indicated for the treatment of relapsing forms of multiple sclerosis (RMS), binds to a unique conformational epitope, thereby depleting B cells very efficiently and allowing subcutaneous administration at lower doses. OBJECTIVES: The aims were to characterize the relationship between ofatumumab concentration and B cell levels, including the effect of covariates such as body weight, age, or baseline B cell count, and use simulations to confirm the chosen therapeutic dose. METHODS: Graphical and regression analyses previously performed based on data from a dose-range finding study provided the B cell depletion target used in the present work. All available adult phase 2/3 data for ofatumumab in RMS patients were pooled to develop a population pharmacokinetics (PK)-B cell count model, using nonlinear mixed-effects modeling. The population PK-B cell model was used to simulate B cell depletion and repletion times and the effect of covariates on PK and B cell metrics, as well as the dose response across a range of subcutaneous ofatumumab monthly doses. RESULTS: The final PK-B cell model was developed using data from 1486 patients. The predetermined B cell target was best achieved and sustained with the 20-mg dose regimen, with median B cell count reaching 8 cells/µL in 11 days and negligible repletion between doses. Only weight had a significant effect on PK, which did not translate into any clinically relevant effect on B cell levels. CONCLUSION: The PK-B cell modeling confirms the dose chosen for the licensed ofatumumab regimen and demonstrates no requirement for dose adjustment based on adult patient characteristics.

Implantation of Acellular Nerve Allograft Using Nerve Connectors.

Background: Nerve connectors are short nerve conduits used to approximate nerve ends. Acellular nerve allografts are viable alternatives when direct repair is not possible but do not produce exudate essential for fibrin clot formation. We hypothesize that acellular nerve allograft implanted using nerve connectors must have end-to-end contact with the in situ nerve stumps to support nerve regeneration. Methods: Sixty Sprague Dawley rats underwent a 14-mm unilateral tibial nerve injury and subsequent repair using various combinations of acellular nerve grafts and nerve connectors. Proximal repairs for all groups utilized direct contact with the nerve stump within connector. Variations in distal repair methods (allograft length, nerve gap, and connector length) defined our 4 groups-group A: 14 mm allograft, no distal gap, and distal connector; group B: 11.5 mm allograft, 2.5 mm distal gap, and distal connector; group C: 9 mm allograft, 5 mm distal gap, and distal connector; group D: 14 mm allograft, no distal gap, and no distal connector. At 3 months post-repair, function and histomorphology were assessed. Results: Developed muscle force was significantly lower in group C (0.073 ± 0.077 N) compared with the other 3 groups (group A = 0.529 ± 0.312 N, group B = 0.461 ± 0.462 N, and group D = 0.409 ± 0.327 N). Axon counts were significantly lower in group C (2121 ± 389) compared with group A (6401 ± 855), group B (4710 ± 755), and group D (4450 ± 126). There was no statistically significant difference in G-ratios (myelination) between groups (P > .05). Conclusion: Nerve regeneration was significantly impaired as the gap distance between the distal end of the allograft and the distal nerve stump increased to 5 mm.

Does partial muscle reinnervation preserve future re-innervation potential?

INTRODUCTION: Late revision nerve surgery for incomplete motor recovery due to partial reinnervation would improve muscle function if all muscle fibers were protected from developing denervation atrophy. METHODS: Sixty immature Sprague-Dawley rats underwent the following tibial nerve manipulations (n = 15/group): group A, partial denervation (two thirds of nerve resected and the remaining one third crushed), revision repair at 8 months; group B, partial denervation; group C, complete denervation, immediate reconstruction; group D, complete denervation, reconstruction at 8 months; and group E, control. Final testing at 11 months included muscle force, weight, and histology. RESULTS: Muscle weight was significantly (P < 0.05) different among all groups (highest to lowest: E > B > C > A > D), and force was significantly lower in groups A and D compared with E. Muscle fiber cross-sectional area was statistically smaller in group A than in groups B, C, or E. DISCUSSION: Partial reinnervation still allowed substantial muscle recovery, but it did not preserve the non-innervated muscle fibers. Muscle Nerve 56: 1143-1148, 2017.

Population pharmacokinetics of IND/GLY (indacaterol/glycopyrronium) in COPD patients.

OBJECTIVE: Indacaterol/glycopyrronium (IND/GLY) is a fixed-dose combination (FDC) of indacaterol, an inhaled long-acting ß2-agonist (LABA), and glycopyrronium, an inhaled long-acting muscarinic antagonist (LAMA), developed as a maintenance bronchodilator treatment for patients with chronic obstructive pulmonary disease (COPD). A population pharmacokinetic (PK) analysis was performed to describe the PK profiles of indacaterol and glycopyrronium following the twice daily (b.i.d.) and once daily (o.d.) inhalation regimens as FDC or as monotherapies and to determine the effect of covariates. METHODS: PK data in 556 COPD patients were pooled from three phase 3 studies. Two phase 3 studies investigated IND/GLY 27.5/12.5 µg b.i.d. and the third study investigated IND/GLY 110/50 µg o.d. Body weight was included in the model with fixed allometric coefficients for indacaterol and glycopyrronium. RESULTS: Statistically significant effects of smoking, age, and sex on apparent clearance of indacaterol; smoking, and estimated glomerular filtration rate at baseline on apparent clearance and Japanese ethnicity on apparent central volume of distribution of glycopyrronium were identified. CONCLUSION: Systemic exposure to indacaterol and glycopyrronium was shown to be dose-proportional and time-independent following inhalation either as monotherapies or FDC. None of the identified covariate effects was judged to be clinically relevant. There is no PK drug-drug interaction between indacaterol and glycopyrronium in its FDC.

Mammals of Australia's tropical savannas: a conceptual model of assemblage structure and regulatory factors in the Kimberley region.

We construct a state-and-transition model for mammals in tropical savannas in northern Australia to synthesize ecological knowledge and understand mammalian declines. We aimed to validate the existence of alternative mammal assemblage states similar to those in arid Australian grasslands, and to speculate on transition triggers. Based on the arid grassland model, we hypothesized that assemblages are partitioned across rainfall gradients and between substrates. We also predicted that assemblages typical of arid regions in boom periods would be prevalent in savannas with higher and more regular rainfall. Data from eight mammal surveys from the Kimberley region, Western Australia (1994 to 2011) were collated. Survey sites were partitioned across rainfall zones and habitats. Data allowed us to identify three assemblage states: State 0:--low numbers of mammals, State II:--dominated by omnivorous rodents and State III:--dominated by rodents and larger marsupials. Unlike arid grasslands, assemblage dominance by insectivorous dasyurids (State I) did not occur in savannas. Mammal assemblages were partitioned across rainfall zones and between substrates as predicted, but-unlike arid regions-were not related strongly to yearly rainfall. Mammal assemblage composition showed high regional stability, probably related to high annual rainfall and predictable wet season resource pulses. As a consequence, we speculate that perpetually booming assemblages in savannas allow top-down control of the ecosystem, with suppression of introduced cats by the dingo, the region's top predator. Under conditions of low or erratic productivity, imposed increasingly by intense fire regimes and introduced herbivore grazing, dingoes may not limit impacts of cats on native mammals. These interacting factors may explain contemporary declines of savanna mammals as well as historical declines in arid Australia. The cat-ecosystem productivity hypothesis raised here differs from the already-articulated cat-habitat structure hypothesis for mammal declines, and we suggest approaches for explicit testing of transition triggers for competing hypotheses.

Evaluation of the pre-posterior distribution of optimized sampling times for the design of pharmacokinetic studies.

Information theoretic methods are often used to design studies that aim to learn about pharmacokinetic and linked pharmacokinetic-pharmacodynamic systems. These design techniques, such as D-optimality, provide the optimum experimental conditions. The performance of the optimum design will depend on the ability of the investigator to comply with the proposed study conditions. However, in clinical settings it is not possible to comply exactly with the optimum design and hence some degree of unplanned suboptimality occurs due to error in the execution of the study. In addition, due to the nonlinear relationship of the parameters of these models to the data, the designs are also locally dependent on an arbitrary choice of a nominal set of parameter values. A design that is robust to both study conditions and uncertainty in the nominal set of parameter values is likely to be of use clinically. We propose an adaptive design strategy to account for both execution error and uncertainty in the parameter values. In this study we investigate designs for a one-compartment first-order pharmacokinetic model. We do this in a Bayesian framework using Markov-chain Monte Carlo (MCMC) methods. We consider log-normal prior distributions on the parameters and investigate several prior distributions on the sampling times. An adaptive design was used to find the sampling window for the current sampling time conditional on the actual times of all previous samples.

Population pharmacokinetics and optimal design of paediatric studies for famciclovir.

AIMS: To develop a population pharmacokinetic model for penciclovir (famciclovir is a prodrug of penciclovir) in adults and children and suggest an appropriate dose for children. Furthermore, to develop a limited sampling design based on sampling windows for three different paediatric age groups (1-2, 2-5 and 5-12 years) using an adequate number of subjects for future pharmacokinetic studies. METHODS: Penciclovir plasma data from six different adult and paediatric studies were supplied by Novartis. Population pharmacokinetic modelling was undertaken in NONMEM version VI. Simulations in MATLAB were used to select an oral paediatric dose that gives similar exposure to 500 mg in adults. Optimal sampling times and sampling windows were obtained in MATLAB and simulations in NONMEM were used to select adequate sample sizes for three paediatric age groups. RESULTS: A two-compartment, first-order absorption model with an absorption lag time, allometric weight models on V(1), V(2) and Q, and an allometric weight model, age and creatinine clearance as covariates on CL adequately describe the pharmacokinetics of penciclovir in adults and children. Estimated CL (l h(-1) 70 kg(-1)) and V(ss) (l.70 kg(-1)) were 31.2 and 83.1, respectively. An oral dose of 10 mg kg(-1) body weight in children was predicted to give similar exposure as 500 mg in adults. A single sampling windows design (0.25-0.4, 0.5-1, 1.25-1.75, 2.75-3.5 and 7.25-8 h) for five samples per subject and 10 subjects in each of the paediatric age groups is recommended for future studies. CONCLUSIONS: A population pharmacokinetic model of penciclovir in adults and children has been developed. A prospective study design, including dose adjustment, cohort size and blood sampling design has been recommended.

Combining MCMC with 'sequential' PKPD modelling.

We introduce a method for preventing unwanted feedback in Bayesian PKPD link models. We illustrate the approach using a simple example on a single individual, and subsequently demonstrate the ease with which it can be applied to more general settings. In particular, we look at the three 'sequential' population PKPD models examined by Zhang et al. (J Pharmacokinet Pharmacodyn 30:387-404, 2003; J Pharmacokinet Pharmacodyn 30:405-416, 2003), and provide graphical representations of these models to elucidate their structure. An important feature of our approach is that it allows uncertainty regarding the PK parameters to propagate through to inferences on the PD parameters. This is in contrast to standard two-stage approaches whereby 'plug-in' point estimates for either the population or the individual-specific PK parameters are required.

Incorporating correlation in interindividual variability for the optimal design of multiresponse pharmacokinetic experiments.

This paper presents a method for optimal design of multiresponse population pharmacokinetic experiments taking into account correlations between interindividual variances. Expressions for the population Fisher information matrix have been defined for uniresponse and multiresponse pharmacokinetic experiments. A major assumption often made is that the variance-covariance matrix of the interindividual variance components has only diagonal elements so that whenever intersubject covariance elements are present, they are ignored during the design of the experiment. Recently expressions that accounted for these off diagonal elements were developed for uniresponse population pharmacokinetic experiments. The work presented here extends these expressions to multiresponse population pharmacokinetic experiments. These were applied to a population pharmacokinetic model, a population pharmacokinetic-pharmacodynamic model, and a parent-metabolite pharmacokinetic model example. The results obtained showed that optimal designs are different with diagonal omega matrix and full omega matrix and ignoring the off diagonal elements can lead to a design that produces more biased and less precise parameter estimates compared to a design that includes the off diagonal elements. The results also showed correlation between residual components of the responses has an effect on the optimal design.

Bayesian sample size for exploratory clinical trials incorporating historical data.

This paper presents a simple Bayesian approach to sample size determination in clinical trials. It is required that the trial should be large enough to ensure that the data collected will provide convincing evidence either that an experimental treatment is better than a control or that it fails to improve upon control by some clinically relevant difference. The method resembles standard frequentist formulations of the problem, and indeed in certain circumstances involving 'non-informative' prior information it leads to identical answers. In particular, unlike many Bayesian approaches to sample size determination, use is made of an alternative hypothesis that an experimental treatment is better than a control treatment by some specified magnitude. The approach is introduced in the context of testing whether a single stream of binary observations are consistent with a given success rate p(0). Next the case of comparing two independent streams of normally distributed responses is considered, first under the assumption that their common variance is known and then for unknown variance. Finally, the more general situation in which a large sample is to be collected and analysed according to the asymptotic properties of the score statistic is explored.

Filaggrin mutations are genetic modifying factors exacerbating X-linked ichthyosis.

Mutations inactivating the STS gene cause X-linked ichthyosis (XLI), whereas null mutations in the FLG gene cause ichthyosis vulgaris. Two brothers presented with XLI. One had a typical fine scaling, and the other was much more severely affected. Both patients carried STS missense mutation T165I. Furthermore, the more severely affected patient also carried heterozygous FLG mutation R501X, which was absent from his mildly affected brother. These data suggest that disrupting epidermal differentiation via different pathways can increase phenotypic severity. Owing to the high population frequency of FLG mutations, filaggrin is a possible genetic modifier in other genodermatoses.

Optimal design for multiresponse pharmacokinetic-pharmacodynamic models - dealing with unbalanced designs.

This paper addresses the problem of determining D-optimal designs for multiresponse pharmacokinetic-pharmacodynamic (PKPD) experiments where data on each response variable can be collected at different times. Most previous multiresponse model optimal design applications have considered the case where all response variables are measured at the same time points. However in practice it may not be possible to have all responses measured at the same sampling times. We propose an optimal design method to take into account the unbalanced nature of the problem. The method developed was applied to a PKPD problem that involved describing the time course of drug plasma concentrations, heart rate and mean arterial blood pressure for both a fixed effects and mixed effects regression model. Additionally a simulation study was carried out in NONMEM for one such population optimal design problem.

A program for individual and population optimal design for univariate and multivariate response pharmacokinetic-pharmacodynamic models.

The design of pharmacokinetic and pharmacodynamic experiments concerns a number of issues, among which are the number of observations and the times when they are taken. Often a model is used to describe these data and the pharmacokinetic-pharmacodynamic behavior of a drug. Knowledge of the data analysis model at the design stage is beneficial for collecting patient data for parameter estimation. A number of criteria for model-oriented experiments, which maximize the information content of the data, are available. In this paper we present a program, Popdes, to investigate the D-optimal design of individual and population multivariate response models, such as pharmacokinetic-pharmacodynamic, physiologically based pharmacokinetic, and parent drug and metabolites models. A pre-clinical and clinical pharmacokinetic-pharmacodynamic model describing the concentration-time profile and effect of an oncology compound in development is used for illustration.

Sample size calculations based on generalized estimating equations for population pharmacokinetic experiments.

We present a method for calculating the sample size of a pharmacokinetic study analyzed using a mixed effects model within a hypothesis testing framework. A sample size calculation method for repeated measurement data analyzed using generalized estimating equations has been modified for nonlinear models. The Wald test is used for hypothesis testing of pharmacokinetic parameters. A marginal model for the population pharmacokinetic is obtained by linearizing the structural model around the subject specific random effects. The proposed method is general in that it allows unequal allocation of subjects to the groups and accounts for situations where different blood sampling schedules are required in different groups of patients. The proposed method has been assessed using Monte Carlo simulations under a range of scenarios. NONMEM was used for simulations and data analysis and the results showed good agreement.

Optimum blood sampling time windows for parameter estimation in population pharmacokinetic experiments.

Clinical trials requiring the collection of pharmacokinetic information often specify blood samples to be taken at fixed times. This may be feasible when trial participants are in a controlled environment such as in early phase clinical trials, however it becomes problematic in trials where patients are in an out-patient clinic setting such as in late phase drug development. In such a situation it is common to take blood samples when it is convenient for all involved and may result in data that are uninformative. This paper proposes an approach to pharmacokinetic study design that allows greater flexibility as to when blood samples can be taken and still result in data that allows satisfactory parameter estimation. The sampling window approach proposed in this paper is based on determining time intervals around the D-optimum pharmacokinetic sampling times. These intervals are determined by allowing the sampling window design to result in a specified level of efficiency when compared to the fixed times D-optimum design. Several approaches are suggested for dealing with this design problem.

The use of a modified Fedorov exchange algorithm to optimise sampling times for population pharmacokinetic experiments.

We propose a new algorithm for optimising sampling times for population pharmacokinetic experiments using D-optimality. The algorithm was used in conjunction with the population Fisher information matrix as implemented in MATLAB (PFIM 1.1 and 1.2) to evaluate population pharmacokinetic designs. The new algorithm based on the classical Fedorov exchange algorithm optimises the determinant of the population Fisher information matrix. The performance of the new algorithm has been compared with other existing algorithms including simplex, simulated annealing and adaptive random search. The new algorithm performed better especially when dealing with complex designs at the expense of longer computing times.

A program for the optimum design of pharmacokinetic, pharmacodynamic, drug metabolism and drug-drug interaction models.

Planning any experiment includes issues such as how many samples are to be taken and their location given some predictor variable. Often a model is used to explain these data; hence including this formally in the design will be beneficial for any subsequent parameter estimation and modelling. A number of criteria for model oriented experiments, which maximise the information content of the collected data are available. In this paper we present a program, Optdes, to investigate the optimal design of pharmacokinetic, pharmacodynamic, drug metabolism and drug-drug interaction models. Using the developed software the location of either a predetermined number of design points (exact designs) or together with the proportion of samples at each point (continuous designs) can be determined. Local as well as Bayesian designs can be optimised by either D- or A-optimality criteria. Although often the optimal design cannot be applied for practical reasons, alternative designs can be readily evaluated.