Model-based meta-analysis of the time to first acute urinary retention or benign prostatic hyperplasia-related surgery in patients with moderate or severe symptoms

AIMS

Combination therapy of 5α-reductase inhibitor and α-blocker is a guideline-endorsed therapeutic approach for patients with moderate-to-severe lower urinary tract symptoms or benign prostatic hyperplasia (LUTS/BPH) who are at risk of disease progression. We aimed to disentangle the contribution of clinical and demographic baseline characteristics affecting the risk of acute urinary retention or BPH-related surgery (AUR/S) from the effect of treatment with drugs showing symptomatic and disease-modifying properties.

METHODS

A time-to-event model was developed using pooled data from patients (n = 10 238) enrolled into six clinical studies receiving placebo, tamsulosin, dutasteride or tamsulosin-dutasteride combination therapy. A parametric hazard function was used to describe the time to first AUR/S. Covariate model building included the assessment of relevant clinical and demographic factors on baseline hazard. Predictive performance was evaluated by graphical and statistical methods.

RESULTS

An exponential hazard model best described the time to first AUR/S in this group of patients. Baseline International Prostate Symptom Score, prostate-specific antigen, prostate volume and maximum urine flow were identified as covariates with hazard ratio estimates of 1.04, 1.08, 1.01 and 0.91, respectively. Dutasteride monotherapy and tamsulosin-dutasteride combination therapy resulted in a significant reduction in the baseline hazard (56.8% and 66.4%, respectively). By contrast, the effect of tamsulosin did not differ from placebo.

CONCLUSIONS

Our analysis showed the implications of disease-modifying properties of dutasteride and tamsulosin-dutasteride combination therapy for the risk of AUR/S. It also elucidated the contribution of different baseline characteristics to the risk of these events. The use of tamsulosin monotherapy (symptomatic treatment) has no impact on individual long-term risk.

Detecting placebo and drug effects on Parkinson's disease symptoms by longitudinal item-score models

This study tested the hypothesis that analyzing longitudinal item scores of the Unified Parkinson's Disease Rating Scale could allow a smaller trial size and describe a drug's effect on symptom progression. Two historical studies of the dopaminergic drug ropinirole were analyzed: a cross-over formulation comparison trial in 161 patients with early-stage Parkinson's disease, and a 24-week, parallel-group, placebo-controlled efficacy trial in 393 patients with advanced-stage Parkinson's disease. We applied item response theory to estimate the patients' symptom severity and developed a longitudinal model using the symptom severity to describe the time course of the placebo response and the drug effect on the time course. Similarly, we developed a longitudinal model using the total score. We then compared sample size needs for drug effect detection using these two different models. Total score modeling estimated median changes from baseline at 24 weeks (90% confidence interval) of -3.7 (-5.4 to -2.0) and -9.3 (-11 to -7.3) points by placebo and ropinirole. Comparable changes were estimated (with slightly higher precision) by item-score modeling as -2.0 (-4.0 to -1.0) and -9.0 (-11 to -8.0) points. The treatment duration was insufficient to estimate the symptom progression rate; hence the drug effect on the progression could not be assessed. The trial sizes to detect a drug effect with 80% power on total score and on symptom severity were estimated (at the type I error level of 0.05) as 88 and 58, respectively. Longitudinal item response analysis could markedly reduce sample size; it also has the potential for assessing drug effects on disease progression in longer trials.

Impact of early vs. delayed initiation of dutasteride/tamsulosin combination therapy on the risk of acute urinary retention or BPH-related surgery in LUTS/BPH patients with moderate-to-severe symptoms at risk of disease progression

Purpose

To evaluate the effect of delayed start of combination therapy (CT) with dutasteride 0.5 mg and tamsulosin 0.4 mg on the risk of acute urinary retention or benign prostatic hyperplasia (BPH)-related surgery (AUR/S) in patients with moderate-to-severe lower urinary tract symptoms (LUTS) at risk of disease progression.

Methods

Using a time-to-event model based on pooled data from 10,238 patients from Phase III/IV dutasteride trials, clinical trial simulations (CTS) were performed to assess the risk of AUR/S up to 48 months in moderate-to-severe LUTS/BPH patients following immediate and delayed start of CT for those not responding to tamsulosin monotherapy. Simulation scenarios (1300 subjects/arm) were investigated, including immediate start (reference) and alternative delayed start (six scenarios 1–24 months). AUR/S incidence was described by Kaplan–Meier survival curves and analysed using log-rank test. The cumulative incidence of events as well as the relative and attributable risks were summarised stratified by treatment.

Results

Survival curves for patients starting CT at month 1 and 3 did not differ from those who initiated CT immediately. By contrast, significant differences (p < 0.001) were observed when switch to CT occurs ≥ 6 months from the initial treatment. At month 48, AUR/S incidence was 4.6% vs 9.5%, 11.0% and 11.3% in patients receiving immediate CT vs. switchers after 6, 12 and 24 months, respectively.

Conclusions

Start of CT before month 6 appears to significantly reduce the risk of AUR/S compared with delayed start by ≥ 6 months. This has implications for the treatment algorithm for men with LUTS/BPH at risk of disease progression.

Electronic supplementary material

The online version of this article (10.1007/s00345-020-03517-0) contains supplementary material, which is available to authorized users.

Model-based meta-analysis of individual International Prostate Symptom Score trajectories in patients with benign prostatic hyperplasia with moderate or severe symptoms

Aims

International Prostate Symptom Score (IPSS) is a marker of lower urinary tract symptoms (LUTS) deterioration or improvement in benign prostate hyperplasia (BPH). Whereas changes in IPSS relative to baseline have been used as endpoints in clinical trials, little attention has been given to the time course of symptoms. The current investigation aimed to develop a drug‐disease model to describe individual IPSS trajectories in moderate and severe BPH patients.

Methods

A model‐based meta‐analytical approach was used including data from 10 238 patients enrolled into Phase III and IV studies receiving placebo, tamsulosin, dutasteride or combination therapy over a period of up to 4 years. Model predictive performance was assessed using statistical and graphical criteria. Subsequently, simulations were performed to illustrate the implications of treatment with drugs showing symptomatic and disease‐modifying properties in patients with varying disease progression rates.

Results

Improvement and worsening of IPSS could be characterized by a model including a sigmoid function which disentangles drug effects from placebo and varying disease progression rates on IPSS. Mean estimate (95% confidence intervals) for the disease progression rate was 0.319 (0.271–0.411) month⁻¹. Treatment effect on IPSS (DELTA) was found to be 0.0605, 0.0139 and 0.0310 month⁻¹ for placebo, tamsulosin and combination therapy, respectively. In addition, it appears that individual trajectories can be clustered together into different phenotypes describing the underlying disease progression rate (i.e. slow, moderate and fast progressors).

Conclusions

The availability of a drug‐disease model enables the evaluation of interindividual differences in disease progression rate, deterioration of symptoms and treatment effects on LUTS/BPH.

Experimental Pharmacology in Transgenic Rodent Models of Alzheimer's Disease

This Mini Review discusses the merits and shortfalls of transgenic (tg) rodents modeling aspects of the human Alzheimer’s disease (AD) pathology and their application to evaluate experimental therapeutics. It addresses some of the differences between mouse and rat tg models for these investigations. It relates, in a condensed fashion, the experience of our research laboratory with the application of anti-inflammatory compounds and S-adenosylmethionine (SAM) at the earliest stages of AD-like amyloid pathology in tg mice. The application of SAM was intended to revert the global brain DNA hypomethylation unleashed by the intraneuronal accumulation of amyloid-β-immunoreactive material, an intervention that restored levels of DNA methylation including of the bace1 gene. This review also summarizes experimental pharmacology observations made in the McGill tg rat model of AD-like pathology by applying “nano-lithium” or a drug with allosteric M1 muscarinic and sigma 1 receptor agonistic properties (AF710B). Extremely low doses of lithium (up to 400 times lower than used in the clinic) had remarkable beneficial effects on lowering pathology and improving cognitive functions in tg rats. Likewise, AF710B treatment, even at advanced stages of the pathology, displayed remarkable beneficial effects. This drug, in experimental conditions, demonstrated possible “disease-modifying” properties as pathology was frankly diminished and cognition improved after a month of “wash-out” period. The Mini-Review ends with a discussion on the predictive value of similar experimental pharmacological interventions in current rodent tg models. It comments on the validity of some of these approaches for early interventions at preclinical stages of AD, interventions which may be envisioned once definitive diagnosis of AD before clinical presentation is made possible.

Pharmacokinetics of Rytary®, An Extended-Release Capsule Formulation of Carbidopa-Levodopa

Parkinson's disease (PD) is a chronic progressive neurological disorder characterized by resting tremor, rigidity, bradykinesia, gait disturbance, and postural instability. Levodopa, the precursor to dopamine, coadministered with carbidopa or benserazide, aromatic amino acid decarboxylase inhibitors, is the most effective and widely used therapeutic agent in the treatment of PD. With continued levodopa treatment, a majority of patients develop motor complications such as dyskinesia and motor 'on-off' fluctuations, which are, in part, related to the fluctuations in plasma concentrations of levodopa. A new extended-release (ER) carbidopa-levodopa capsule product (also referred to as IPX066) was developed and approved in the US as Rytary^® and in the EU as Numient^®. The capsule formulation is designed to provide an initial rapid absorption of levodopa comparable to immediate-release (IR) carbidopa-levodopa, and to subsequently provide stable levodopa concentrations with reduced peak-to-trough excursions in plasma concentrations in order to reduce motor fluctuations associated with pulsatile stimulation of dopamine receptors and to minimize dyskinesia. Phase III studies of this ER carbidopa-levodopa capsule formulation in patients with PD have shown a significant reduction in 'off' time compared with IR carbidopa-levodopa and carbidopa-levodopa-entacapone. We present a review of the clinical pharmacokinetics and pharmacodynamics of this ER product of carbidopa-levodopa in healthy subjects and in patients with PD.

Opportunities and pitfalls in clinical proof-of-concept: principles and examples

Clinical proof-of-concept trials crucially inform major resource deployment decisions. This paper discusses several mechanisms for enhancing their rigour and efficiency. The importance of careful consideration when using a surrogate endpoint is illustrated; situational effectiveness of run-in patient enrichment is explored; a versatile tool is introduced to ensure a strong pharmacological underpinning; the benefits of dose-titration are revealed by simulation; and the importance of adequately scheduled observations is shown. The general process of model-based trial design and analysis is described and several examples demonstrate the value in historical data, simulation-guided design, model-based analysis and trial adaptation informed by interim analysis.

Dose-Response Analysis of the Effect of Carbidopa-Levodopa Extended-Release Capsules (IPX066) in Levodopa-Naive Patients With Parkinson Disease

Parkinson disease is an age-related disorder of the central nervous system principally due to loss of dopamine-producing cells in the midbrain. Levodopa, in combination with carbidopa, is widely regarded as an effective treatment for the symptoms of Parkinson disease. A dose-response relationship is established for carbidopa-levodopa extended-release capsules (IPX066) in levodopa-naive Parkinson disease patients using a disease progression model. Unified Parkinson Disease Rating Scale (UPDRS) part II plus part III scores from 171 North American patients treated with placebo or IPX066 for approximately 30 weeks from a double-blind, parallel-group, dose-ranging study were used to develop the pharmacodynamic model. The model comprised 3 components: a linear function describing disease progression, a component describing placebo (or nonlevodopa) effects, and a component to describe the effect of levodopa. Natural disease progression in early Parkinson disease as measured by UPDRS was 11.6 units/year and faster in patients with more severe disease (Hoehn-Yahr stage 3). Maximum placebo/nonlevodopa response was 23.0% of baseline UPDRS. Maximum levodopa effect from IPX066 was 76.7% of baseline UPDRS, and the ED50 was 450 mg levodopa. Equilibration half-life for the effect compartment was 62.8 days. Increasing age increased and being female decreased equilibration half-life. The quantitative model allowed description of the entire time course of response to clinical trial intervention.

Clinical pharmacology = disease progression + drug action

Clinical pharmacology is concerned with understanding how to use medicines to treat disease. Pharmacokinetics and pharmacodynamics have provided powerful methodologies for describing the time course of concentration and effect in individuals and in populations. This population approach may also be applied to describing the progression of disease and the action of drugs to change disease progress. Quantitative models for symptomatic and disease-modifying effects of drugs are valuable not only for describing drugs and diseases but also for identifying criteria to distinguish between types of drug actions, with implications for regulatory decisions and long-term patient care.

Osteoarthritis disease progression model using six year follow-up data from the osteoarthritis initiative

The objective was to develop a quantitative model of disease progression of knee osteoarthritis over 6 years using the total WOMAC score from patients enrolled into the Osteoarthritis Initiative (OAI) study. The analysis was performed using data from the Osteoarthritis Initiative database. The time course of the total WOMAC score of patients enrolled into the progression cohort was characterized using non-linear mixed effect modeling in NONMEM. The effect of covariates on the status of the disease and the progression rate was investigated. The final model provided a good description of the experimental data using a linear progression model with a common baseline (19 units of the total WOMAC score). The WOMAC score decreased by 1.77 units/year in 89% of the population or increased by 1.74 units/year in 11% of the population. Multiple covariates were found to affect the baseline and the rate of progression, including BMI, sex, race, the use of pain medications, and the limitation in activity due to symptoms. A mathematical model to describe the disease progression of osteoarthritis in the studied population was developed. The model identified two sub-populations with increasing or decreasing total WOMAC score over time, and the effect of important covariates was quantified.

Pharmacokinetic-pharmacodynamic modeling of antipsychotic drugs in patients with schizophrenia Part I: the use of PANSS total score and clinical utility

BACKGROUND

To develop a pharmacokinetic-pharmacodynamic (PK-PD) model using individual-level data of Positive and Negative Syndrome Scale (PANSS) total score to characterize the antipsychotic drug effect taking into account the placebo effect and dropout rate. In addition, a clinical utility (CU) criterion that describes the usefulness of a drug therapy was calculated using the efficacy of the drug and dropout rates.

METHODS

Data from 12 clinical trials in schizophrenia patients was used to quantify the effects of the antipsychotic drugs (APs), namely, haloperidol, risperidone, olanzapine, ziprasidone and paliperidone. Compartmental PK models were used to describe the time course of plasma drug concentrations. The combination of an Emax and the Weibull model was used to describe the drug and placebo effects. The steady-state drug concentrations were assumed to be the drivers of the exposure-response relationship. An exponential model was utilized to identify the predictors of probability of dropout. Simulations were performed to check the predictability of the model, and to calculate the CU of the drugs based on PANSS scores and dropout rates.

RESULTS

The maximal drug effect (E(max)) was highest for olanzapine whilst it was lowest for ziprasidone. Higher observed PANSS scores resulted in a greater likelihood of dropout. Taking into account the efficacy and the drop-out rate, all APs possessed a comparable CU at the therapeutic doses. The resulting PK-PD model parameters were used to compute the effective concentration and dose required to produce a clinically meaningful 30% drop in PANSS score from the baseline.

CONCLUSIONS

The developed PK-PD model and the associated CU score allow the evaluation of the time course of the PANSS scores of the different APs and a proper comparison of their clinically relevant treatments effects.

Modelling and simulation in the pharmaceutical industry--some reflections

Modelling and simulation (M&S) is increasingly being applied in (clinical) drug development. It provides an opportune area for the community of pharmaceutical statisticians to pursue. In this article, we highlight useful principles behind the application of M&S. We claim that M&S should be focussed on decisions, tailored to its purpose and based in applied sciences, not relying entirely on data-driven statistical analysis. Further, M&S should be a continuous process making use of diverse information sources and applying Bayesian and frequentist methodology, as appropriate. In addition to forming a basis for analysing decision options, M&S provides a framework that can facilitate communication between stakeholders. Besides the discussion on modelling philosophy, we also describe how standard simulation practice can be ineffective and how simulation efficiency can often be greatly improved.

Structural models describing placebo treatment effects in schizophrenia and other neuropsychiatric disorders

Large variation in placebo response within and among clinical trials can substantially affect conclusions about the efficacy of new medications in psychiatry. Developing a robust placebo model to describe the placebo response is important to facilitate quantification of drug effects, and eventually to guide the design of clinical trials for psychiatric treatment via a model-based simulation approach. In addition, high dropout rates are very common in the placebo arm of psychiatric clinical trials. While developing models to evaluate the effect of placebo response, the data from patients who drop out of the trial should be considered for accurate interpretation of the results. The objective of this paper is to review the various empirical and semi-mechanistic models that have been used to quantify the placebo response in schizophrenia trials. Pros and cons of each placebo model are discussed. Additionally, placebo models used in other neuropsychiatric disorders like depression, Alzheimer's disease and Parkinson's disease are also reviewed with the objective of finding those placebo models that could be useful for clinical studies of both acute and chronic schizophrenic disease conditions. Better understanding of the patterns of dropout and the factors leading to dropouts are crucial in identifying the true placebo response. We therefore also review dropout models that are used in the development of models for treatment effects and in the optimization of clinical trials by simulation approaches. The use of an appropriate modelling strategy that is capable of identifying the potential sources of variable placebo responses and dropout rates is recommended for improving the sensitivity in discriminating between the effects of active treatment and placebo.

Coping with time scales in disease systems analysis: application to bone remodeling

In this study we demonstrate the added value of mathematical model reduction for characterizing complex dynamic systems using bone remodeling as an example. We show that for the given parameter values, the mechanistic RANK-RANKL-OPG pathway model proposed by Lemaire et al. (J Theor Biol 229:293-309, 2004) can be reduced to a simpler model, which can describe the dynamics of the full Lemaire model to very good approximation. The response of both models to changes in the underlying physiology and therapeutic interventions was evaluated in four physiologically meaningful scenarios: (i) estrogen deficiency/estrogen replacement therapy, (ii) Vitamin D deficiency, (iii) ageing, and (iv) chronic glucocorticoid treatment and its cessation. It was found that on the time scale of disease progression and therapeutic intervention, the models showed negligible differences in their dynamic properties and were both suitable for characterizing the impact of estrogen deficiency and estrogen replacement therapy, Vitamin D deficiency, ageing, and chronic glucocorticoid treatment and its cessation on bone forming (osteoblasts) and bone resorbing (osteoclasts) cells. It was also demonstrated how the simpler model could help in elucidating qualitative properties of the observed dynamics, such as the absence of overshoot and rebound, and the different dynamics of onset and washout.

Rationale for delayed-start study of pramipexole in Parkinson's disease: the PROUD study

Perhaps the most important unmet need in Parkinson's disease (PD) is the ability to slow or prevent progression of the neurodegeneration that underlies the motor and nonmotor features of this disorder. Pramipexole, a dopamine agonist used for the symptomatic treatment of PD, has demonstrated neuroprotective properties in laboratory studies. The PRamipexole On Underlying Disease (PROUD) study is a randomized, double-blind clinical trial evaluating the ability of pramipexole to modify disease progression using a delayed-start design. PD patients (n = 535) with mean age 62.5 years, mean duration since diagnosis of 4.4 months, and mean total Unified Parkinson's disease Rating Scale (UPDRS) score of 24.5 were recruited. In Phase I, patients were randomly assigned to be titrated to 1.5 mg pramipexole or placebo and maintained on study drug for 6-9 months. In Phase II, all patients were titrated to 1.5 mg pramipexole and maintained on study drug until the end of the study at 15 months. No rescue medication was allowed in the protocol. The primary endpoint is the change in total UPDRS score (parts I-III) from baseline to 15 months. A range of secondary endpoints separately assess UPDRS subscales, quality of life, depression, and impulse control disorders. A sub-study examined dopamine transporter uptake scans at baseline and 15 months. The results of PROUD will provide insight into the potential for early versus delayed treatment with pramipexole to modify motor outcome at 15 months in recently diagnosed PD patients.

Clinical trials of disease-modifying therapies for neurodegenerative diseases: the challenges and the future

Neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease represent a crucial and exponentially increasing challenge to health care systems throughout the world. There is an urgent need for effective treatments that will both delay their onset and slow their inexorable progression. Many obstacles stand in the way of realizing these goals. It is expected that future advances will have a major impact on how and when the diagnosis will be made. It is hoped that these will eventually make it possible to initiate effective disease-modifying therapies long before the neurodegenerative process becomes established and symptomatic.

Review: Efficient Rehabilitation Trial Designs Using Disease Progress Modeling: A Pediatric Traumatic Brain Injury Example

Background. The identification of possible treatment effects against a background of spontaneous recovery is a major challenge to the successful completion of randomized clinical trials (RCTs) in rehabilitation research. Conventional trial outcomes such as the differences between group means of an outcome measure at a fixed time point are inefficient to an extent that is a major problem, particularly for exploratory studies seeking preliminary evidence of efficacy. Objective . To quantitate gains in study power over conventional fixed-end-point designs by using parametric end points derived from the modeling of the time course of recovery after brain injury. Methods. Nonlinear mixed effects (NLME) modeling of the recovery trajectories of 103 children rehabilitating after traumatic brain injury (TBI) as reflected in serial WeeFIM scores was performed. Pseudoreplicate data sets were generated replicating the statistical characteristics of the original data set, and these formed the basis of clinical trial simulations to derive robust estimates of study power. Results. Parametric end points derived from modeling of recovery improve study power (and reduce necessary sample size) by up to 5 times in this example. Conclusions. Parametric end points derived from models of recovery trajectories offer an efficient alternative design for exploratory clinical studies of rehabilitation interventions.

Endpoints and analyses to discern disease-modifying drug effects in early Parkinson's disease

Parkinson's disease is an age-related degenerative disorder of the central nervous system that often impairs the sufferer's motor skills and speech, as well as other functions. Symptoms can include tremor, stiffness, slowness of movement, and impaired balance. An estimated four million people worldwide suffer from the disease, which usually affects people over the age of 60. Presently, there is no precedent for approving any drug as having a modifying effect (i.e., slowing or delaying) for disease progression of Parkinson's disease. Clinical trial designs such as delayed start and withdrawal are being proposed to discern symptomatic and protective effects. The current work focused on understanding the features of delayed start design using prior knowledge from published and data submitted to US Food and Drug Administration (US FDA) as part of drug approval or protocol evaluation. Clinical trial simulations were conducted to evaluate the false-positive rate, power under a new statistical analysis methodology, and various scenarios leading to patient discontinuations from clinical trials. The outcome of this work is part of the ongoing discussion between the US FDA and the pharmaceutical industry on the standards required for demonstrating disease-modifying effect using delayed start design.