The relationship between weight gain during chemotherapy and outcomes in patients with advanced non-small cell lung cancer.

BACKGROUND: This post hoc, pooled analysis examined the relationship between different weight gain categories and overall survival (OS) in patients with non-small cell lung cancer (NSCLC) receiving first-line platinum-based chemotherapy. METHODS: Data were pooled from the control arms of three phase III clinical studies (NCT00596830, NCT00254891, and NCT00254904), and the maximum weight gain in the first 3 months from treatment initiation was categorised as >0%, >2.5%, and >5.0%. Cox proportional hazard modelling of OS was used to estimate hazard ratios (HRs) for each category, including baseline covariates, time to weight gain, and time to confirmed objective response (RECIST Version 1.0). RESULTS: Of 1030 patients with advanced NSCLC (IIIB 11.5% and IV 88.5%), 453 (44.0%), 252 (24.5%), and 120 (11.7%) experienced weight gain from baseline of >0%, >2.5%, and >5.0%, respectively. The median time to weight gain was 23 (>0%), 43 (>2.5%), and 45 (>5.0%) days. After adjusting for a time-dependent confirmed objective response, the risk of death was reduced for patients with any weight gain (>0% vs. ≤0% [HR 0.71; 95% confidence interval-CI 0.61, 0.82], >2.5% vs. ≤2.5% [HR 0.76; 95% CI 0.64, 0.91] and >5.0% vs. ≤5.0% [HR 0.77; 95% CI 0.60, 0.99]). The median OS was 13.5 versus 8.6 months (weight gain >0% vs. ≤0%), 14.4 versus 9.4 months (weight gain >2.5% vs. ≤2.5%), and 13.4 versus 10.2 months (weight gain >5.0% vs. ≤5.0%). CONCLUSIONS: Weight gain during treatment was associated with a reduced risk of death, independent of tumour response. The survival benefit was comparable for weight gain >0%, >2.5%, and >5.0%, suggesting that any weight gain may be an early predictor of survival with implications for the design of interventional cancer cachexia studies.

Predicting Treatment Responses in Patients With Osteoarthritis: Results From Two Phase III Tanezumab Randomized Clinical Trials.

Prediction of treatment responses is essential to move forward translational science. Our question was to identify patient-based variables that predicted responses to treatments. We conducted secondary analyses on pooled data from two randomized phase III clinical trials (NCT02697773 and NCT02709486) conducted in participants with moderate to severe osteoarthritis randomized to subcutaneous placebo (n = 514) or tanezumab 2.5 mg (n = 514). We used gradient boosted regression trees to identify variables that predicted Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) Pain subscale scores at Week 16 and marginal plots to determine the directional relationship between each variable category and responses to placebo or tanezumab within the models. We also used Virtual Twins models to identify potential subgroups of response to the active treatment vs. placebo. We found that responses to placebo were predicted by baseline WOMAC Physical Function, baseline WOMAC Pain, the radiographic classification of the index joint, and the standard deviation of diary pain scores at baseline. In contrast, baseline WOMAC Pain along with failure of prior medications, duration of disease, and standard deviation of diary pain scores at baseline were predictive of tanezumab responses as expressed by the WOMAC Pain scores at Week 16. Those who responded to tanezumab vs. placebo were identified based on the radiographic classification of the index joint and either age or smoking status. These secondary-data analyses identified distinct and common patient-based variables to predict response to placebo or tanezumab. These findings will inform the design of future clinical trials, helping to move forward clinical pharmacology and translational science.

Multimethod quantitative benefit-risk assessment of treatments for moderate-to-severe osteoarthritis.

OBJECTIVE: Demonstrate how benefit-risk profiles of systemic treatments for moderate-to-severe osteoarthritis (OA) can be compared using a quantitative approach accounting for patient preference. STUDY DESIGN AND SETTING: This study used a multimethod benefit-risk modelling approach to quantifiably compare treatments of moderate-to-severe OA. In total four treatments and placebo were compared. Comparisons were based on four attributes identified as most important to patients. Patient Global Assessment of Osteoarthritis was included as a favourable effect. Unfavourable effects, or risks, included opioid dependence, nonfatal myocardial infarction and rapidly progressive OA leading to total joint replacement. Clinical data from randomized clinical trials, a meta-analysis of opioid dependence and a long-term study of celecoxib were mapped into value functions and weighted with patient preferences from a discrete choice experiment. RESULTS: Lower-dose NGFi had the highest weighted net benefit-risk score (0.901), followed by higher-dose NGFi (0.889) and NSAIDs (0.852), and the lowest score was for opioids (0.762). Lower-dose NGFi was the highest-ranked treatment option even when assuming a low incidence (0.34% instead of 4.7%) of opioid dependence (ie, opioid benefit-risk score 808) and accounting for both the uncertainty in clinical effect estimates (first rank probability 46% vs 20% for NSAIDs) and imprecision in patient preference estimates (predicted choice probability 0.26, 95% confidence interval [CI] 0.25-0.28 vs 0.21, 95% CI 0.19-0.23 for NSAIDs). CONCLUSION: The multimethod approach to quantitative benefit-risk modelling allowed the interpretation of clinical data from the patient perspective while accounting for uncertainties in the clinical effect estimates and imprecision in patient preferences.

Gender, age, disease severity, body mass index and diabetes may not affect response to subcutaneous tanezumab in patients with osteoarthritis after 16 weeks of treatment. A subgroup analysis of placebo-controlled trials.

AIM: To assess the impact of pre-specified patient characteristics on efficacy and safety of subcutaneous tanezumab in patients with osteoarthritis (OA). METHODS: Data were pooled from two (efficacy; N = 1545) or three (safety; N = 1754) phase 3 placebo-controlled trials. Change from baseline to week 16 in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) Pain, WOMAC Physical Function and patient global assessment of OA (PGA-OA) scores was compared between tanezumab (2.5 and 5 mg) and placebo groups via analysis of covariance. Treatment-emergent adverse events (TEAEs) were summarised descriptively. Analyses were done in patient subgroups (men or women; age <65, ≥65, or ≥75 years; body mass index [BMI] <25, 25 to <30, 30 to <35 or ≥35 kg/m2 ; diabetes or no diabetes; baseline WOMAC Pain score <7 or ≥7; and Kellgren-Lawrence [KL] grades 2, 3 or 4 in the index joint) and the overall population. RESULTS: In all subgroups, improvements in WOMAC Pain were numerically greater and often statistically significant (P < .05) for both tanezumab groups compared with placebo. Results were similar for WOMAC Physical Function and PGA-OA. TEAE profiles were generally consistent across subgroups and similar to the overall population (ie slightly higher rates of TEAEs, serious TEAEs and severe TEAEs with tanezumab relative to placebo) with a few exceptions. Exceptions included women reporting slightly more TEAEs with tanezumab than men, and patients with diabetes reporting slightly more severe TEAEs with tanezumab than patients without diabetes. Additionally, TEAEs were more frequent with tanezumab than placebo in the age ≥65 and ≥75 years, but not the age <65 years, subgroups. CONCLUSIONS: Efficacy and safety/tolerability of tanezumab may not be meaningfully impacted by gender, age, BMI, diabetes status, baseline pain severity or KL grade in the index joint. Conclusions are limited by low patient number in some subgroups. Clinicaltrials.gov: NCT02697773, NCT02709486, NCT01089725.

Pregabalin for neuropathic pain in primary care settings: recommendations for dosing and titration.

Pregabalin is one of the first-line treatments approved for the management of neuropathic pain (NeP). While many patients benefit from treatment with pregabalin, they are often treated with suboptimal doses, possibly due to unfamiliarity around prescribing the drug and/or side effects that can occur with up-titration. This narrative review discusses key aspects of initiating, titrating, and managing patients prescribed pregabalin therapy, and addresses concerns around driving and the potential for abuse, as well as when to seek specialist opinion. To ensure that patients derive maximum therapeutic benefit from the drug, we suggest a 'low and slow' dosing approach to limit common side effects and optimize tolerability alongside patients' expectations. When requiring titration to higher doses, we recommend initiating 'asymmetric dosing,' with the larger dose in the evening. Fully engaging patients in order for them to understand the expected timeline for efficacy and side effects (including their resolution), can also help determine the optimal titration tempo for each individual patient. The 'low and slow' approach also recognizes that patients with NeP are heterogeneous in terms of their optimal therapeutic dose of pregabalin. Hence, it is recommended that general practitioners closely monitor patients and up-titrate according to pain relief and side effects to limit suboptimal dosing or premature discontinuation.

Integrating Machine Learning With Microsimulation to Classify Hypothetical, Novel Patients for Predicting Pregabalin Treatment Response Based on Observational and Randomized Data in Patients With Painful Diabetic Peripheral Neuropathy.

PURPOSE: Variability in patient treatment responses can be a barrier to effective care. Utilization of available patient databases may improve the prediction of treatment responses. We evaluated machine learning methods to predict novel, individual patient responses to pregabalin for painful diabetic peripheral neuropathy, utilizing an agent-based modeling and simulation platform that integrates real-world observational study (OS) data and randomized clinical trial (RCT) data. PATIENTS AND METHODS: The best supervised machine learning methods were selected (through literature review) and combined in a novel way for aligning patients with relevant subgroups that best enable prediction of pregabalin responses. Data were derived from a German OS of pregabalin (N=2642) and nine international RCTs (N=1320). Coarsened exact matching of OS and RCT patients was used and a hierarchical cluster analysis was implemented. We tested which machine learning methods would best align candidate patients with specific clusters that predict their pain scores over time. Cluster alignments would trigger assignments of cluster-specific time-series regressions with lagged variables as inputs in order to simulate "virtual" patients and generate 1000 trajectory variations for given novel patients. RESULTS: Instance-based machine learning methods (k-nearest neighbor, supervised fuzzy c-means) were selected for quantitative analyses. Each method alone correctly classified 56.7% and 39.1% of patients, respectively. An "ensemble method" (combining both methods) correctly classified 98.4% and 95.9% of patients in the training and testing datasets, respectively. CONCLUSION: An ensemble combination of two instance-based machine learning techniques best accommodated different data types (dichotomous, categorical, continuous) and performed better than either technique alone in assigning novel patients to subgroups for predicting treatment outcomes using microsimulation. Assignment of novel patients to a cluster of similar patients has the potential to improve prediction of patient outcomes for chronic conditions in which initial treatment response can be incorporated using microsimulation. CLINICAL TRIAL REGISTRIES: www.clinicaltrials.gov: NCT00156078, NCT00159679, NCT00143156, NCT00553475.

Novel study design to assess the efficacy and tolerability of antiseizure medications for focal-onset seizures in infants and young children: A consensus document from the regulatory task force and the pediatric commission of the International League against Epilepsy (ILAE), in collaboration with the Pediatric Epilepsy Research Consortium (PERC).

High-quality placebo-controlled drug trials for focal-onset seizures in infants and children younger than 4 years have become increasingly difficult to perform because of eligibility constraints and onerous study designs. Traditional designs used in these populations require a high baseline seizure frequency, two hospitalizations for video-electroencephalography (video-EEG) monitoring, and willingness to accept potential exposure to placebo when the drugs to be tested are usually already available for off-label prescription. To address these constraints, the International League Against Epilepsy (ILAE) regulatory taskforce and the ILAE pediatric commission, in collaboration with the Pediatric Epilepsy Research Consortium (PERC), propose a novel trial design which involves seizure counting by caregivers based on previous video-EEG/video validation of specific seizure semiologies. We present a novel randomized placebo-controlled trial design intended to be used for studying new antiseizure medications (ASMs) for focal-onset seizures (FOS) in children aged one month to four years. This design uses "time to Nth seizure" as the primary outcome and incorporates a new element of variable baseline duration. This approach permits enrollment of infants with lower seizure burden, who might not have video-EEG-recorded seizures within 2-3 days of monitoring. Repeated hospitalizations for video-EEG recordings are avoided, and duration of baseline and exposure to placebo or ineffective treatment(s) are minimized. By broadening eligibility criteria, reducing risks from prolonged placebo exposure, and relying on validated recording of seizure counting by caregivers, clinical trials will be likely to be completed more efficiently than in the recent past.

The relationship between the reporting of euphoria events and early treatment responses to pregabalin: an exploratory post-hoc analysis.

BACKGROUND: Euphoria is a complex, multifactorial problem that is reported as an adverse event in clinical trials of analgesics including pregabalin. The relationship between the reporting of euphoria events and pregabalin early treatment responses was examined in this exploratory post-hoc analysis. METHODS: Data were from patients with neuropathic or non-neuropathic chronic pain enrolled in 40 randomized clinical trials, who received pregabalin (75-600 mg/day) or placebo. Reports of treatment-emergent euphoria events were based on the Medical Dictionary of Regulatory Activities preferred term "euphoric mood". Prevalence rates of euphoria events overall and by indication were assessed. Post-treatment endpoints included ≥30% improvements in pain and sleep scores up to 3 weeks as well as a ≥1-point improvement in daily pain score up to 11 days after treatment. RESULTS: 13,252 patients were analyzed; 8,501 (64.1%) and 4,751 (35.9%) received pregabalin and placebo, respectively. Overall, 1.7% (n=222) of patients reported euphoria events. Among pregabalin-treated patients, a larger proportion who reported euphoria events achieved an early pain response compared with those who did not report euphoria (30% pain responders in week 1 with euphoria events [43.0%], without euphoria events [24.2%]). Results were similar for weeks 2 and 3. For Days 2-11, a larger proportion of pregabalin-treated patients with (relative to without) euphoria events were 1-point pain responders. Findings were similar in pregabalin-treated patients for sleep endpoints (30% sleep responders in week 1 with euphoria events [50.7%], without euphoria events [36.1%]). Similar results were found for weeks 2 and 3. Patients who received placebo showed similar patterns, although the overall number of them who reported euphoria events was small (n=13). CONCLUSION: In patients who received pregabalin for neuropathic or non-neuropathic chronic pain, those who experienced euphoria events may have better early treatment responses than those who did not report euphoria events.

Assessing the Value of Time Series Real-World and Clinical Trial Data vs. Baseline-Only Data in Predicting Responses to Pregabalin Therapy for Patients with Painful Diabetic Peripheral Neuropathy.

BACKGROUND AND OBJECTIVE: Treatment challenges necessitate new approaches to customize care to individual patient needs. Integrating data from randomized controlled trials and observational studies may reduce potential covariate biases, yielding information to improve treatment outcomes. The objective of this study was to predict pregabalin responses, in individuals with painful diabetic peripheral neuropathy, by examining time series data (lagged inputs) collected after treatment initiation vs. baseline using microsimulation. METHODS: The platform simulated pregabalin-treated patients to estimate hypothetical future pain responses over 6 weeks based on six distinct time series regressions with lagged variables as inputs (hereafter termed "time series regressions"). Data were from three randomized controlled trials (N = 398) and an observational study (N = 3159). Regressions were derived after performing a hierarchical cluster analysis with a matched patient dataset from coarsened exact matching. Regressions were validated using unmatched (observational study vs. randomized controlled trial) patients. Predictive implications (of 6-week outcomes) were compared using only baseline vs. 1- to 2-week prior data. RESULTS: Time series regressions for pain performed well (adjusted R2 0.85-0.91; root mean square error 0.53-0.57); those with only baseline data performed less well (adjusted R2 0.13-0.44; root mean square error 1.11-1.40). Simulated patient distributions yielded positive predictive values for > 50% pain score improvements from baseline for the six clusters (287-777 patients each; range 0.87-0.98). CONCLUSIONS: Effective prediction of pregabalin response for painful diabetic peripheral neuropathy was accomplished through combining cluster analyses, coarsened exact matching, and time series regressions, reflecting distinct patterns of baseline and "on-treatment" variables. These results advance the understanding of microsimulation to predict patient treatment responses through integration and inter-relationships of multiple, complex, and time-dependent characteristics.

WHY COMBINE DIFFERENT DATA SOURCES?: Analyzing the tremendous amount of patient data can provide meaningful insights to improve healthcare quality. Using statistical methods to combine data from clinical trials with real-world studies can improve overall data quality (e.g., reducing biases related to real-world patient variability). WHY CONSIDER A TIME SERIES ANALYSIS?: The best predictor of future outcomes is past outcomes. A "time series" collects data at regular intervals over time. Statistical analyses of time series data allow us to discern time-dependent patterns to predict future clinical outcomes. Modeling and simulation make it possible to combine enormous amounts of data from clinical trial databases to predict a patient's clinical response based on data from similar patients. This approach improves selecting the right drug/dose for the right patient at the right time (i.e., personalized medicine). Using modeling and simulation, we predicted which patients would show a positive response to pregabalin (a neuropathic pain drug) for painful diabetic peripheral neuropathy. WHAT ARE THE MAJOR FINDINGS AND IMPLICATIONS?: For pregabalin-treated patients, a time series analysis had substantially more predictive value vs. analysis only of baseline data (i.e., data collected at treatment initiation). The ability to best predict which patients will respond to therapy has the overall implication of better informing drug treatment decisions. For example, an appropriate modeling and simulation platform complete with relevant historical clinical data could be integrated into a stand-alone device used to monitor and also predict a patient's response to therapy based on daily outcome measures (e.g., smartphone apps, wearable technologies).

Correction: Using time series analysis approaches for improved prediction of pain outcomes in subgroups of patients with painful diabetic peripheral neuropathy.

[This corrects the article DOI: 10.1371/journal.pone.0207120.].

Using time series analysis approaches for improved prediction of pain outcomes in subgroups of patients with painful diabetic peripheral neuropathy.

Prior work applied hierarchical clustering, coarsened exact matching (CEM), time series regressions with lagged variables as inputs, and microsimulation to data from three randomized clinical trials (RCTs) and a large German observational study (OS) to predict pregabalin pain reduction outcomes for patients with painful diabetic peripheral neuropathy. Here, data were added from six RCTs to reduce covariate bias of the same OS and improve accuracy and/or increase the variety of patients for pain response prediction. Using hierarchical cluster analysis and CEM, a matched dataset was created from the OS (N = 2642) and nine total RCTs (N = 1320). Using a maximum likelihood method, we estimated weekly pain scores for pregabalin-treated patients for each cluster (matched dataset); the models were validated with RCT data that did not match with OS data. We predicted novel 'virtual' patient pain scores over time using simulations including instance-based machine learning techniques to assign novel patients to a cluster, then applying cluster-specific regressions to predict pain response trajectories. Six clusters were identified according to baseline variables (gender, age, insulin use, body mass index, depression history, pregabalin monotherapy, prior gabapentin, pain score, and pain-related sleep interference score). CEM yielded 1766 patients (matched dataset) having lower covariate imbalances. Regression models for pain performed well (adjusted R-squared 0.90-0.93; root mean square errors 0.41-0.48). Simulations showed positive predictive values for achieving >50% and >30% change-from-baseline pain score improvements (range 68.6-83.8% and 86.5-93.9%, respectively). Using more RCTs (nine vs. the earlier three) enabled matching of 46.7% more patients in the OS dataset, with substantially reduced global imbalance vs. not matching. This larger RCT pool covered 66.8% of possible patient characteristic combinations (vs. 25.0% with three original RCTs) and made prediction possible for a broader spectrum of patients. Trial Registration: www.clinicaltrials.gov (as applicable): NCT00156078, NCT00159679, NCT00143156, NCT00553475.

Efficacy of pregabalin in post-traumatic peripheral neuropathic pain: a randomized, double-blind, placebo-controlled phase 3 trial.

The growing need for symptomatic treatment of post-traumatic neuropathic pain (PTNP) continues to be unmet. Studies evaluating the efficacy of pregabalin for reducing neuropathic pain following trauma and surgery yielded positive results over ≤ 8-week treatment. To assess the efficacy and tolerability of pregabalin over 3 months in patients with PTNP, a randomized, double-blind, placebo-controlled, parallel-group trial evaluated patients with PTNP at 101 centers in 11 countries-the longest, largest such trial. Adults diagnosed with PTNP were randomly assigned (1:1) to 15 weeks of pregabalin (flexibly dosed 150-600 mg/day) or matching placebo. Primary efficacy analysis was by mixed-model repeated measures comparing change from baseline to week 15 in weekly mean pain scores between active and placebo groups. Evaluable patients included 274 in the pregabalin group and 265 in the placebo group. Trauma was surgical in 49.6% of patients, non-surgical in the remainder. The primary efficacy analysis showed no statistically significant difference between pregabalin and placebo groups in the change from baseline to week 15 [mean difference, - 0.22 points (95% confidence interval, 0.54-0.10); p = 0.1823]. However, comparisons for key secondary outcome measures yielded p values < 0.05 favoring pregabalin. Consistent with the known safety profile of pregabalin, the most common adverse events were dizziness and somnolence (14.6 and 9.9% of patients, respectively) with pregabalin (vs 4.2 and 3.4% with placebo). These findings demonstrate the feasibility of conducting a large, phase 3 registration trial in the heterogeneous PTNP study population.ClinicalTrials.gov NCT01701362.

Comparison of the efficacy and safety of pregabalin for postherpetic neuralgia in Chinese and international patients.

PURPOSE: Pregabalin is indicated for postherpetic neuralgia (PHN) in multiple countries, including China. This analysis compared pregabalin efficacy and safety in Chinese and international patients with PHN. PATIENTS AND METHODS: Data from Chinese and international randomized, double-blind, placebo-controlled trials were compared. Pregabalin was administered at fixed (150, 300, or 600 mg/day) or flexible (150-600 mg/day) doses. The main efficacy measure was mean pain score change at endpoint on an 11-point numeric rating scale ranging from 0 = no pain to 10 = worst possible pain. Secondary efficacy measures included proportions of 30% and 50% pain responders, pain-related sleep interference (PRSI) scores, and proportions of Patient Global Impression of Change (PGIC) responders. The incidences of serious adverse events (SAEs) and adverse events (AEs) were used to assess safety. The effect of baseline pain severity on efficacy was tested. The proportions of patients with severe baseline pain who had moderate or mild pain at endpoint were also assessed. RESULTS: A total of 1166 patients were analyzed: 312 Chinese and 854 international. Overall, results were similar between Chinese and international patients. Pregabalin statistically significantly improved mean pain score versus placebo (least squares mean difference [95% CIs]: Chinese, -0.8 [-1.2, -0.5]; international, -1.3 [-1.6, -1.0]; both p<0.001). Pregabalin was statistically significantly better than placebo in Chinese and international patient groups in the proportions of 30% and 50% pain responders, PRSI scores, and proportions of PGIC responders. Baseline pain severity did not affect efficacy, except for some measures in Chinese patients with moderate baseline pain. Similar proportions of pregabalin-treated patients with severe baseline pain had moderate or mild pain at endpoint in both groups. SAE and AE profiles were comparable in Chinese and international patient groups, except incidences were commonly higher in international patients. CONCLUSION: Chinese and international patients with PHN exhibit comparable pregabalin efficacy and safety, highlighting the utility of pregabalin for diverse PHN patient populations.

Prediction of therapeutic response to pregabalin in subjects with neuropathic pain.

OBJECTIVE: To evaluate four models based on potential predictors for achieving a response to pregabalin treatment for neuropathic pain (NeP). METHODS: In total, 46 pain studies were screened, of which 27 NeP studies met the criteria for inclusion in this analysis. Data were pooled from these 27 placebo-controlled randomized trials to assess if baseline characteristics (including mean pain and pain-related sleep interference [PRSI] scores), early clinical response during weeks 1-3 of treatment (change from baseline in pain and PRSI scores), and presence of treatment-emergent adverse events (AEs) were predictive of therapeutic response. Therapeutic response was defined as a ≥30% reduction from baseline in either pain and/or PRSI scores at week 5 with supplemental analyses to predict pain outcomes at weeks 8 and 12. Predictors of Patient Global Impression of Change (PGIC) were also evaluated. Four models were assessed: Random Forest, Logistic Regression, Naïve Bayes, and Partial Least Squares. RESULTS: The number of pregabalin-treated subjects in the training/test datasets, respectively, were 2818/1407 (30% pain analysis), 2812/1405 (30% sleep analysis), and 2693/1345 (PGIC analysis). All four models demonstrated consistent results, and the most important predictors of treatment outcomes at week 5 and pain outcomes at weeks 8 and 12 were the reduction in pain score and sleep score in the first 1-3 weeks. The presence or absence of the most common AEs in the first 1-3 weeks was not correlated with any treatment outcome. CONCLUSIONS: Subjects with an early response to pregabalin are more likely to experience an end-of-treatment response.

An evidence-based review of pregabalin for the treatment of fibromyalgia.

OBJECTIVES: Pregabalin, an α2-δ agonist, is approved for the treatment of fibromyalgia (FM) in the United States, Japan, and 37 other countries. The purpose of this article was to provide an in-depth, evidence-based summary of pregabalin for FM as demonstrated in randomized, placebo-controlled clinical studies, including open-label extensions, meta-analyses, combination studies and post-hoc analyses of clinical study data. METHODS: PubMed was searched using the term "pregabalin AND fibromyalgia" and the Cochrane Library with the term "pregabalin". Both searches were conducted on 2 March 2017 with no other date limits set. RESULTS: Eleven randomized, double-blind, placebo-controlled clinical studies were identified including parallel group, two-way crossover and randomized withdrawal designs. One was a neuroimaging study. Five open-label extensions were also identified. Evidence of efficacy was demonstrated across the studies identified with significant and clinically relevant improvements in pain, sleep quality and patient status. The safety and tolerability profile of pregabalin is consistent across all the studies identified, including in adolescents, with dizziness and somnolence the most common adverse events reported. These efficacy and safety data are supported by meta-analyses (13 studies). Pregabalin in combination with other pharmacotherapies (7 studies) is also efficacious. Post-hoc analyses have demonstrated the onset of pregabalin efficacy as early as 1-2 days after starting treatment, examined the effect of pregabalin on other aspects of sleep beyond quality, and shown it is effective irrespective of the presence of a wide variety of patient demographic and clinical characteristics. CONCLUSIONS: Pregabalin is a treatment option for FM; its clinical utility has been comprehensively demonstrated.

Time Series Disturbance Detection for Hypothesis-Free Signal Detection in Longitudinal Observational Databases.

INTRODUCTION: Signal detection remains a cornerstone activity of pharmacovigilance. Routine quantitative signal detection primarily focuses on screening of spontaneous reports. In striving to enhance quantitative signal detection capability further, other data streams are being considered for their potential contribution as sources of emerging signals, one of which is longitudinal observational databases, including electronic medical record (EMR) and transactional insurance claims databases. Quantitative signal detection on such databases is a nascent field-with published methods being primarily based either on individual metrics, which may not effectively represent the complexity of the longitudinal records and their necessary variation for analysis for drug-outcome pairs, or on visualization discovery approaches leveraging multiple aspects of the records, which are not particularly tractable to high-throughput hypothesis-free signal detection. One extensively tested example of the latter is chronographs. METHODS: We apply a disturbance detection algorithm to chronographs using UK EMR The Health Improvement Network (THIN) data. The algorithm utilizes autoregressive integrated moving average (ARIMA)-based time series methodology designed to find disturbances that occur outside the normal pattern trends of the ARIMA model for the chronograph. Chronographs currently highlight drug-event pairs as potentially worthy of further clinical assessment, via filter-based increases in disproportionality scores from before to after the index drug exposure, tested across a range of case and control windows. RESULTS: We replicate the findings on six exemplar chronographs from a previous publication, and show how disturbances can be effectively detected across this set of pairs. Further, 692 disturbances were detected in analysis of all 384 individual READ code outcomes ever recorded 50 or more times for patients prescribed sibutramine. The disturbances are algorithmically further broken into subsets of clinical interest. CONCLUSION: Overall, the disturbance algorithm approach shows promising capacity for detecting outliers, and shows tractability of the algorithmic approach for large-scale screening. The method offers an array of pattern types for detection and clinical review.

Dose-response of pregabalin for diabetic peripheral neuropathy, postherpetic neuralgia, and fibromyalgia.

OBJECTIVES: The pregabalin dose-response for pain, Patient Global Impression of Change (PGIC), and sleep quality measures in painful diabetic peripheral neuropathy (pDPN), postherpetic neuralgia (PHN), and fibromyalgia (FM) is relevant for physicians treating these patients. This analysis aimed to demonstrate the dose-response of pregabalin for each indication and describe the onset (incidence), onset/continuation (prevalence), and resolution of adverse events (AEs) occurring during treatment. METHODS: Data from 14 placebo-controlled, fixed-dose pregabalin trials in pDPN, PHN, and FM were pooled within each indication. Patients had mean baseline pain scores ≥6 on an 11-point numeric rating scale. A hyperbolic Emax dose-response model examined the dose-response of pregabalin for pain, PGIC, and sleep quality. Safety assessments included onset and prevalence of common AEs each week, and resolution in the first 2 months of treatment. RESULTS: In all indications, the likelihood of patients experiencing pain relief and improvements in PGIC and sleep quality increased in a dose-dependent manner with increasing doses. In all indications, new incidences of dizziness and somnolence were highest after 1 week of treatment, with few subsequent new reports at a given dose. Prevalence rates decreased steadily after 1 week of treatment. In FM, new onset weight gain emerged 6-8 weeks following treatment; prevalence rates generally increased then remained steady over time. With the exception of weight gain, many AEs resolved in month 1. CONCLUSION: The dose-response of pregabalin for pain, PGIC, and sleep quality was demonstrated, highlighting the benefit of achieving the maximum recommended dose of 300 mg/day for pDPN, 300-600 mg/day for PHN, and 300-450 mg/day for FM. Common AEs are generally seen within 1 week of starting treatment, with few subsequent new reports at a given dose. New onset weight gain occurs after 6 weeks of treatment, reinforcing the need for regular monitoring of weight.

Integrating data from randomized controlled trials and observational studies to predict the response to pregabalin in patients with painful diabetic peripheral neuropathy.

BACKGROUND: More patient-specific medical care is expected as more is learned about variations in patient responses to medical treatments. Analytical tools enable insights by linking treatment responses from different types of studies, such as randomized controlled trials (RCTs) and observational studies. Given the importance of evidence from both types of studies, our goal was to integrate these types of data into a single predictive platform to help predict response to pregabalin in individual patients with painful diabetic peripheral neuropathy (pDPN). METHODS: We utilized three pivotal RCTs of pregabalin (398 North American patients) and the largest observational study of pregabalin (3159 German patients). We implemented a hierarchical cluster analysis to identify patient clusters in the Observational Study to which RCT patients could be matched using the coarsened exact matching (CEM) technique, thereby creating a matched dataset. We then developed autoregressive moving average models (ARMAXs) to estimate weekly pain scores for pregabalin-treated patients in each cluster in the matched dataset using the maximum likelihood method. Finally, we validated ARMAX models using Observational Study patients who had not matched with RCT patients, using t tests between observed and predicted pain scores. RESULTS: Cluster analysis yielded six clusters (287-777 patients each) with the following clustering variables: gender, age, pDPN duration, body mass index, depression history, pregabalin monotherapy, prior gabapentin use, baseline pain score, and baseline sleep interference. CEM yielded 1528 unique patients in the matched dataset. The reduction in global imbalance scores for the clusters after adding the RCT patients (ranging from 6 to 63% depending on the cluster) demonstrated that the process reduced the bias of covariates in five of the six clusters. ARMAX models of pain score performed well (R 2 : 0.85-0.91; root mean square errors: 0.53-0.57). t tests did not show differences between observed and predicted pain scores in the 1955 patients who had not matched with RCT patients. CONCLUSION: The combination of cluster analyses, CEM, and ARMAX modeling enabled strong predictive capabilities with respect to pain scores. Integrating RCT and Observational Study data using CEM enabled effective use of Observational Study data to predict patient responses.

Effect of Concomitant Pain Medications on Response to Pregabalin in Patients with Postherpetic Neuralgia or Spinal Cord Injury-Related Neuropathic Pain.

BACKGROUND: Patients with neuropathic pain (NeP) often receive combination therapy with multiple agents in the hopes of improving both pain and any comorbidities that may be present. While pregabalin is often recommended as a first-line treatment of NeP, few studies have examined the effects of concomitant medications on the efficacy of pregabalin. OBJECTIVE: To examine the effects of concomitant medications on the efficacy and safety of pregabalin for the treatment of NeP. STUDY DESIGN: Data were derived from 7 randomized placebo-controlled trials of pregabalin (150, 300, 600, and flexible 150 - 600 mg/d) for the treatment of postherpetic neuralgia (PHN) and 2 randomized placebo-controlled trials for the treatment of NeP due to spinal cord injury (SCI-NeP). On each day, patients rated the severity of their pain and pain-related sleep interference (PRSI) over the previous 24 hours on a scale from 0 to 10, with higher scores indicating greater severity. Patients were also continually monitored for the occurrence of adverse events. SETTING: A pooled retrospective analyses of data from randomized clinical trials. METHODS: Changes from baseline in mean weekly pain and PRSI scores were compared between patients who received concomitant NeP medications and patients who did not receive concomitant NeP medications. Results of these comparisons are presented separately for the PHN (through 4, 8, and 12 weeks) and SCI-NeP (through 12 weeks) cohorts. Common adverse events are also presented for each treatment group. RESULTS: Pregabalin significantly improved both pain and PRSI scores relative to placebo at most dose levels and time points examined. Notably, little difference was observed in the extent of therapeutic response to pregabalin between patients who received concomitant NeP medications and patients who did not receive concomitant NeP medications. Additionally, the profile of treatment-emergent adverse events appeared to be largely unaffected by the use of concomitant NeP medications in the pooled patient population. LIMITATIONS: Our analysis is limited in that the original trials of pregabalin were not powered to examine the effects of concomitant NeP medications. CONCLUSIONS: The data presented here demonstrate that therapeutic response to pregabalin and the occurrence of adverse events in patients with NeP are generally unaffected by the concurrent use of other NeP medications.Trial Registration numbers: NCT00159666; NCT00301223; NCT00407745Key words: Pregabalin, neuropathic pain, pain-related sleep interference, concomitant medications, postherpetic neuralgia, spinal cord injury, efficacy, safety.

Early improvement with pregabalin predicts endpoint response in patients with generalized anxiety disorder: an integrated and predictive data analysis.

Generalized anxiety disorder (GAD), a common mental disorder, has several treatment options including pregabalin. Not all patients respond to treatment; quickly determining which patients will respond is an important treatment goal. Patient-level data were pooled from nine phase II and III randomized, double-blind, short-term, placebo-controlled trials of pregabalin for the treatment of GAD. Efficacy outcomes included the change from baseline in the Hamilton Anxiety Scale (HAM-A) total score and psychic and somatic subscales. Predictive modelling assessed baseline characteristics and early clinical responses to determine those predictive of clinical improvement at endpoint. A total of 2155 patients were included in the analysis (1447 pregabalin, 708 placebo). Pregabalin significantly improved the HAM-A total score compared with the placebo at endpoint, treatment difference (95% confidence interval), -2.61 (-3.21 to -2.01), P<0.0001. Pregabalin significantly improved HAM-A psychic and somatic scores compared with placebo, -1.52 (-1.85 to -1.18), P<0.0001, and -1.10 (-1.41 to -0.80), P<0.0001, respectively. Response to pregabalin in the first 1-2 weeks (≥20 or ≥30% improvement in HAM-A total, psychic or somatic score) was predictive of an endpoint greater than or equal to 50% improvement in the HAM-A total score. Pregabalin is an effective treatment option for patients with GAD. Patients with early response to pregabalin are more likely to respond significantly at endpoint.