Impact of disease progression on individual IPSS trajectories and consequences of immediate versus delayed start of treatment in patients with moderate or severe LUTS associated with BPH

Purpose Despite superiority of tamsulosin–dutasteride combination therapy versus monotherapy for lower urinary tract symptoms due to benign prostatic hyperplasia (LUTS/BPH), patients at risk of disease progression are often initiated on α-blockers. This study evaluated the impact of initiating tamsulosin monotherapy prior to switching to tamsulosin–dutasteride combination therapy versus immediate combination therapy using a longitudinal model describing International Prostate Symptom Score (IPSS) trajectories in moderate/severe LUTS/BPH patients at risk of disease progression. Methods Clinical trial simulations (CTS) were performed using data from 10,238 patients from Phase III/IV dutasteride trials. The effect of varying disease progression rates was explored by comparing profiles on- and off-treatment. CTS scenarios were investigated, including a reference (immediate combination therapy) and six alternative virtual treatment arms (delayed combination therapy of 1–24 months). Clinical response (≥ 25% IPSS reduction relative to baseline) was analysed using log-rank test. Differences in IPSS relative to baseline at various on-treatment time points were assessed by t tests. Results Delayed combination therapy initiation led to significant (p < 0.01) decreases in clinical response. At month 48, clinical response rate was 79.7% versus 74.1%, 70.3% and 71.0% and IPSS was 6.3 versus 7.6, 8.1 and 8.0 (switchers from tamsulosin monotherapy after 6, 12 and 24 months, respectively) with immediate combination therapy. More patients transitioned from severe/moderate to mild severity scores by month 48. Conclusions CTS allows systematic evaluation of immediate versus delayed combination therapy. Immediate response to α-blockers is not predictive of long-term symptom improvement. Observed IPSS differences between immediate and delayed combination therapy (6–24 months) are statistically significant. Electronic supplementary material The online version of this article (10.1007/s00345-019-02783-x) contains supplementary material, which is available to authorized users.

Full details of the model-building and evaluation have been described elsewhere [2]. It should be noted that for the purposes of the current analysis, simulations were performed using parameter estimates from a final model in which the observed IPSS at baseline (IPSS 0 ) were assumed to reflect baseline disease state (IPSS b ). Alternative procedures can also be applied to estimate IPSS b if one assumes that the underlying disease state is unknown at the initiation of treatment [3]. Both approaches yield comparable model parameters estimates describing the individual IPSS trajectories.

Simulation-based assessment of the effect of disease progression and treatment on individual IPSS trajectories
Initially, simulations were performed to illustrate implications for treatment response when drugs with disease-modifying properties are used in individual patients with varying disease-progression rates along with the predicted profiles in the absence of any active treatment (Supplementary Figure S1). Because combination therapy produces both symptomatic and disease-modifying effects, the resulting treatment response will depend on the underlying progression rate. In fact, different percentiles of the disease 2 progression (DISP) parameter distribution were used to visualise and distinguish between the range of possible IPSS values due to the underlying progression of disease and treatment response. The interaction between these two factors can be further characterised by the net change from baseline (IPSS) over time. In addition, to assess the impact of disease state, as defined by IPSS severity at baseline, the effect of symptomatic and disease-modifying effects of tamsulosin monotherapy (i.e. symptomatic treatment) and tamsulosin-dutasteride combination therapy (i.e. symptomatic + disease-modifying treatment) on individual IPSS trajectories was simulated and stratified by baseline IPSS (Supplementary Figures S2 and  S3). These profiles demonstrate that baseline IPSS and disease-progression rates interact with treatment effect, making it difficult to disentangle the contribution of each factor to response, which in a typical clinical trial is often defined in terms of relative change from baseline.
It should be noted that in a typical clinical trial setting, appraisal of the rate of progression and its implication for treatment response is confounded by residual variability in IPSS. The relevance of modelpredicted individual IPSS trajectories is emphasised in Supplementary Figure S4, where the impact of measurement noise on the evaluation of IPSS after administration of combination therapy is illustrated for patients with mild, moderate or severe symptoms.
These results also explain why no predefined set of baseline characteristics has been identified as a sufficiently sensitive marker of the deterioration of symptoms or treatment response. In fact, in a recent data-mining exercise including men with LUTS secondary to BPH, it was shown that baseline IPSS severity achieved sensitivity and specificity of 70% and ~50%, respectively, as predictors of individual response to placebo or tadalafil [4]. However, these values are below the sensitivity and specificity threshold of 80% that enables reliable allocation of an individual patient to either the responder or non-responder group [4]. Hence, clinicians cannot accurately predict whether a patient will respond to symptomatic and disease-modifying interventions at the start of treatment. Figure S1. Impact of the disease-modifying properties of tamsulosin-dutasteride combination therapy on the IPSS response in individual patients with varying rates of disease progression. Each panel depicts the IPSS trajectories (upper panels) and the IPSS (lower panels) over 48 months for patients across a range of disease progression rates (2.5 th , 25 th , 50 th , 75 th and 97.5 th percentiles). Red areas demonstrate predicted profiles in the absence of any active treatment; blue areas demonstrate varying progression rates receiving combination therapy; solid lines are mean predicted IPSS; and shaded areas represent the 95% prediction interval (n=200 simulations). The predicted trajectories describing disease progression are depicted, assuming a hypothetical scenario in which patients remain untreated despite deterioration of symptoms. CTS show predicted IPSS without residual errors. Figure S2. Impact of baseline symptom severity on individual IPSS trajectories, and disease-modifying properties of tamsulosin-dutasteride combination therapy in individual patients with comparable rates of disease progression. IPSS trajectories (upper panels) and IPSS (lower panels) over 48 months are depicted for patients with different baseline IPSS (8, 12, 16, 20 and 30). Red areas demonstrate predicted profiles in the absence of any active treatment; blue areas demonstrate varying progression rates receiving combination therapy; solid lines are mean predicted IPSS; and shaded areas represent 95% prediction intervals (n=200 simulations). The predicted trajectories describing disease progression are depicted, assuming a hypothetical scenario in which patients remain untreated despite deterioration of symptoms. CTS show predicted IPSS without residual errors. Figure S3. Impact of symptomatic (tamsulosin monotherapy, upper panel) and symptomatic and diseasemodifying properties (tamsulosin-dutasteride combination therapy, lower panel) on individual IPSS trajectories in patients with varying rates of disease progression and varying IPSS at baseline. Panels are stratified by symptom severity, as defined by IPSS values at baseline. Red areas demonstrate predicted profiles in the absence of any active treatment; blue areas demonstrate varying progression rates receiving either tamsulosin monotherapy (upper panel) or combination therapy (lower panel); solid lines are mean predicted IPSS; and shaded areas represent 95% prediction intervals (n=200 simulations). The predicted trajectories describing disease progression (red) are depicted, assuming a hypothetical scenario in which patients remain untreated despite deterioration of symptoms. Figure S4. Example of simulated individual IPSS trajectories in patients with varying rates of disease progression, after incorporation of residual variability. Panels are stratified by symptom severity, as defined by IPSS values at baseline. Each panel depicts the predicted (line) and 'observed' (dots) IPSS trajectories over 48 months after administration of tamsulosin-dutasteride combination therapy. The predicted trajectories describing disease progression are depicted, assuming a hypothetical scenario in which patients remain untreated despite deterioration of symptoms. Table S1. Overview of the studies identified for the proposed model-based meta-analysis. Protocol title is shown, along with details of treatment type and duration, and the purpose of the study data during model-building and validation procedures.

Patient baseline characteristics:
The patient population randomised and assigned to each CTS was generated taking into account similar inclusion/exclusion criteria as used for the main combination-treatment studies, CombAT and CONDUCT [15,20]. Inclusion criteria (at enrolment) were: men aged ≥50 years, IPSS ≥8 points, prostate volume ≥30 cm 3 , total serum PSA ≥1.5 ng/mL to <10 ng/mL, maximum urine flow >5 mL/s to ≤15 mL/s with a minimum voided volume ≥125 mL. Exclusion criteria comprised history or evidence of prostate cancer, previous prostatic surgery, history of AUR within 3 months prior to study entry, 5-ARI use within 6 months (or dutasteride within 12 months) of entry or use of an -blocker or phytotherapy for BPH within 2 weeks prior to entry.
The simulated baseline distribution of IPSS scores, prostate volume, maximum urinary flow and PSA values ensured patterns comparable to the observed covariate distributions in the pooled data from all studies (listed in Supplementary Table S1).
Similarly, the simulated distribution of all other relevant clinical covariates identified as significant in the final model was based 11 on the observed covariate distributions in the pooled population.

Treatment arms
Patients included in the CTS were assumed to be untreated at baseline. In addition, we assumed the assigned treatment was initiated with minor delays following LUTS/BPH diagnosis. Our seven-arm, virtual trial had one reference and six alternative treatment scenarios:

Statistical methods:
The predicted responder rate [defined as proportion of patients who showed CMI at month 48] was selected as the primary endpoint and analysed using a log-rank test. A t-test was applied to the difference in IPSS relative to baseline at different visits after start of treatment. For completeness, the proportion of subjects transitioning across different symptom-severity groups was calculated for each virtual treatment arm along with the absolute difference in IPSS at different visits up to month 48. The proposed simulated scenarios aimed at reaching statistical significance with high statistical power (>90%) and low type 1 error ( ≤0.05). Whilst usually no predefined effect size has been set for IPSS change from baseline, previous investigations have shown that 90% statistical power is achieved with a group size of 296 patients when assessing mean differences between treatment groups of 1.6, with a standard deviation of 6, and a 0.05 level of significance [15].

Assumptions:
• Interindividual variability in pharmacokinetics was assumed to have a minor impact on treatment response, as the currently approved dose levels of tamsulosin-dutasteride yield nearly maximum pharmacological effect. Variation in response was therefore assigned primarily to interindividual differences in disease-specific parameters.

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• All CTS scenarios were implemented under the assumption of perfect adherence to treatment. In real-life conditions, different adherence patterns may occur, depending on symptom severity and/or comorbidities, which may significantly alter the predicted differences across CTS scenarios.
• As drop-out in real clinical trials appears to be non-informative (i.e. at random), treatment scenarios were implemented without dropout.
• Parameter estimates obtained from the pooled patient database (N=10,236 had dosing records) were assumed to be sufficiently precise to replicate the performance of the treatment in a wider population, as observed in clinical practice.
However, we recognise that inclusion/exclusion criteria may not fully reflect the LUTS/BPH population eligible for treatment with α-blockers and 5-ARI in clinical practice.
5-ARI, 5α-reductase inhibitor; AUR, acute urinary retention; CMI, clinically meaningful improvement; CTS, clinical trial simulations; IPSS, International Prostate Symptom Score; LUTS/BPH, lower urinary tract symptoms due to benign prostatic hyperplasia; PSA, prostate-specific antigen. summarises the impact of immediate combination therapy. Results refer to a single trial replicate including placebo effect only at the initial treatment phase. Placebo effect is a key component of the initial response and can last more than 6 months, as assessed by its half-life. No studies included a placebo treatment arm for >2 years, so it was not possible to establish whether inter-individual differences might allow for a longer placebo effect.