Phosphatidylinositol 3-Kinase -Selective Inhibition With Alpelisib (BYL719) in PIK3CA-Altered Solid Tumors: Results From the First-in-Human Study

PurposeWe report the first-in-human phase Ia study to our knowledge (ClinicalTrials.gov identifier: NCT01219699) identifying the maximum tolerated dose and assessing safety and preliminary efficacy of single-agent alpelisib (BYL719), an oral phosphatidylinositol 3-kinase (PI3K)-selective inhibitor.Patients and MethodsIn the dose-escalation phase, patients with PIK3CA-altered advanced solid tumors received once-daily or twice-daily oral alpelisib on a continuous schedule. In the dose-expansion phase, patients with PIK3CA-altered solid tumors and PIK3CA-wild-type, estrogen receptor-positive/human epidermal growth factor receptor 2-negative breast cancer received alpelisib 400 mg once daily.ResultsOne hundred thirty-four patients received treatment. Alpelisib maximum tolerated doses were established as 400 mg once daily and 150 mg twice daily. Nine patients (13.2%) in the dose-escalation phase had dose-limiting toxicities of hyperglycemia (n = 6), nausea (n = 2), and both hyperglycemia and hypophosphatemia (n = 1). Frequent all-grade, treatment-related adverse events included hyperglycemia (51.5%), nausea (50.0%), decreased appetite (41.8%), diarrhea (40.3%), and vomiting (31.3%). Alpelisib was rapidly absorbed; half-life was 7.6 hours at 400 mg once daily with minimal accumulation. Objective tumor responses were observed at doses 270 mg once daily; overall response rate was 6.0% (n = 8; one patient with endometrial cancer had a complete response, and seven patients with cervical, breast, endometrial, colon, and rectal cancers had partial responses). Stable disease was achieved in 70 (52.2%) patients and was maintained > 24 weeks in 13 (9.7%) patients; disease control rate (complete and partial responses and stable disease) was 58.2%. In patients with estrogen receptor-positive/human epidermal growth factor receptor 2-negative breast cancer, median progression-free survival was 5.5 months. Frequently mutated genes ( 10% tumors) included TP53 (51.3%), APC (23.7%), KRAS (22.4%), ARID1A (13.2%), and FBXW7 (10.5%).ConclusionAlpelisib demonstrated a tolerable safety profile and encouraging preliminary activity in patients with PIK3CA-altered solid tumors, supporting the rationale for selective PI3K inhibition in combination with other agents for the treatment of PIK3CA-mutant tumors

Association of digital measures and self-reported fatigue: a remote observational study in healthy participants and participants with chronic inflammatory rheumatic disease.

Fatigue is a subjective, complex and multi-faceted phenomenon, commonly experienced as tiredness. However, pathological fatigue is a major debilitating symptom associated with overwhelming feelings of physical and mental exhaustion. It is a well-recognized manifestation in chronic inflammatory rheumatic diseases, such as Sjögren's Syndrome and Systemic Lupus Erythematosus and an important predictor of patient's health-related quality of life (HRQoL). Patient reported outcome questions are the key instruments to assess fatigue. To date, there is no consensus about reliable quantitative assessments of fatigue.Observational data for a period of one month were collected from 296 participants in the United States. Data comprised continuous multimodal digital data from Fitbit, including heart rate, physical activity and sleep features, and app-based daily and weekly questions covering various HRQoL factors including pain, mood, general physical activity and fatigue. Descriptive statistics and hierarchical clustering of digital data were used to describe behavioural phenotypes. Gradient boosting classifiers were trained to classify participant-reported weekly fatigue and daily tiredness from multi-sensor and other participant-reported data, and extract a set of key predictive features.Cluster analysis of Fitbit parameters highlighted multiple digital phenotypes, including sleep-affected, fatigued and healthy phenotypes. Features from participant-reported data and Fitbit data both contributed as key predictive features of weekly physical and mental fatigue and daily tiredness. Participant answers to pain and depressed mood-related daily questions contributed the most as top features for predicting physical and mental fatigue, respectively. To classify daily tiredness, participant answers to questions on pain, mood and ability to perform daily activities contributed the most. Features related to daily resting heart rate and step counts and bouts were overall the most important Fitbit features for the classification models.These results demonstrate that multimodal digital data can be used to quantitatively and more frequently augment pathological and non-pathological participant-reported fatigue

Contribution of machine learning to tumor growth inhibition modeling for hepatocellular carcinoma patients under Roblitinib (FGF401) drug treatment.

Machine learning (ML) opens new perspectives in identifying predictive factors of efficacy among a large number of patients' characteristics in oncology studies. The objective of this work was to combine ML with population pharmacokinetic/pharmacodynamic (PK/PD) modeling of tumor growth inhibition to understand the sources of variability between patients and therefore improve model predictions to support drug development decisions. Data from 127 patients with hepatocellular carcinoma enrolled in a phase I/II study evaluating once-daily oral doses of the fibroblast growth factor receptor FGFR4 kinase inhibitor, Roblitinib (FGF401), were used. Roblitinib  PKs was best described by a two-compartment model with a delayed zero-order absorption and linear elimination. Clinical efficacy using the longitudinal sum of the longest lesion diameter data was described with a population PK/PD model of tumor growth inhibition including resistance to treatment. ML, applying elastic net modeling of time to progression data, was associated with cross-validation, and allowed to derive a composite predictive risk score from a set of 75 patients' baseline characteristics. The two approaches were combined by testing the inclusion of the continuous risk score as a covariate on PD model parameters. The score was found as a significant covariate on the resistance parameter and resulted in 19% reduction of its variability, and 32% variability reduction on the average dose for stasis. The final PK/PD model was used to simulate effect of patients' characteristics on tumor growth inhibition profiles. The proposed methodology can be used to support drug development decisions, especially when large interpatient variability is observed

Unsupervised machine-learning algorithms for the identification of clinical phenotypes in the osteoarthritis initiative database.

Osteoarthritis (OA) is a complex disease comprising diverse underlying patho-mechanisms. To enable the development of effective therapies, segmentation of the heterogenous patient population is critical. This study aimed at identifying such patient clusters using two different machine learning algorithms.Using the progression and incident cohorts of the Osteoarthritis Initiative (OAI) dataset, deep embedded clustering (DEC) and multiple factor analysis with clustering (MFAC) approaches, including 157 input-variables at baseline, were employed to differentiate specific patient profiles.DEC resulted in 5 and MFAC in 3 distinct patient phenotypes. Both identified a "comorbid" cluster with higher body mass index (BMI), relevant burden of comorbidity and low levels of physical activity. Both methods also identified a younger and physically more active cluster and an elderly cluster with functional limitations, but low disease impact. The additional two clusters identified with DEC were subgroups of the young/physically active and the elderly/physically inactive clusters. Overall pain trajectories over 9 years were stable, only the numeric rating scale (NRS) for pain showed distinct increase, while physical activity decreased in all clusters. Clusters showed different (though non-significant) trajectories of joint space changes over the follow-up period of 8 years.Two different clustering approaches yielded similar patient allocations primarily separating complex "comorbid" patients from healthier subjects, the latter divided in young/physically active vs elderly/physically inactive subjects. The observed association to clinical (pain/physical activity) and structural progression could be helpful for early trial design as strategy to enrich for patients who may specifically benefit from disease-modifying treatments

A risk analysis of alpelisib-induced hyperglycemia in patients with advanced solid tumors and breast cancer

Abstract Background Hyperglycemia is an on-target effect of PI3Kα inhibitors. Early identification and intervention of treatment-induced hyperglycemia is important for improving management of patients receiving a PI3Kα inhibitor like alpelisib. Here, we characterize incidence of grade 3/4 alpelisib-related hyperglycemia, along with time to event, management, and outcomes using a machine learning model. Methods Data for the risk model were pooled from patients receiving alpelisib ± fulvestrant in the open-label, phase 1 X2101 trial and the randomized, double-blind, phase 3 SOLAR-1 trial. The pooled population (n = 505) included patients with advanced solid tumors (X2101, n = 221) or HR+/HER2− advanced breast cancer (SOLAR-1, n = 284). External validation was performed using BYLieve trial patient data (n = 340). Hyperglycemia incidence and management were analyzed for SOLAR-1. Results A random forest model identified 5 baseline characteristics most associated with risk of developing grade 3/4 hyperglycemia (fasting plasma glucose, body mass index, HbA1c, monocytes, age). This model was used to derive a score to classify patients as high or low risk for developing grade 3/4 hyperglycemia. Applying the model to patients treated with alpelisib and fulvestrant in SOLAR-1 showed higher incidence of hyperglycemia (all grade and grade 3/4), increased use of antihyperglycemic medications, and more discontinuations due to hyperglycemia (16.7% vs. 2.6% of discontinuations) in the high- versus low-risk group. Among patients in SOLAR-1 (alpelisib + fulvestrant arm) with PIK3CA mutations, median progression-free survival was similar between the high- and low-risk groups (11.0 vs. 10.9 months). For external validation, the model was applied to the BYLieve trial, for which successful classification into high- and low-risk groups with shorter time to grade 3/4 hyperglycemia in the high-risk group was observed. Conclusions A risk model using 5 clinically relevant baseline characteristics was able to identify patients at higher or lower probability for developing alpelisib-induced hyperglycemia. Early identification of patients who may be at higher risk for hyperglycemia may improve management (including monitoring and early intervention) and potentially lead to improved outcomes. Registration: ClinicalTrials.gov: NCT01219699 (registration date: October 13, 2010; retrospectively registered), ClinicalTrials.gov: NCT02437318 (registration date: May 7, 2015); ClinicalTrials.gov: NCT03056755 (registration date: February 17, 2017)

Alpelisib (BYL719) plus Fulvestrant in PIK3CA-altered and PIK3CA wild-type ER+ advanced breast cancer. An open-label, phase 1b dose-escalation and expansion study

Importance: The phosphatidylinositol 3-kinase (PI3K) pathway is frequently activated in patients with estrogen receptor-positive (ER+), endocrine therapy-resistant breast cancers. Objective: To assess the maximum tolerated dose (MTD), safety, and activity of alpelisib, an oral, PI3Kα specific inhibitor, plus fulvestrant in patients with ER+ advanced breast cancer (ABC). Design: This was an open-label, single-arm, phase 1b study of alpelisib plus fulvestrant. Setting: Eleven centers in 5 countries. Participants: Postmenopausal women with PIK3CA-altered or PIK3CA-wild-type ER+ ABC, who progressed on or after anti-estrogen therapy. Intervention: Escalating doses of alpelisib once daily (QD), starting from 300-mg, plus fixed-dose fulvestrant (500 mg) in the dose-escalation phase; alpelisib at the recommended phase 2 dose plus fulvestrant in the dose-expansion phase. Main outcomes and measures: The primary endpoint was determination of the MTD of QD alpelisib plus fulvestrant. Secondary endpoints included safety and preliminary activity. Results: From October 2010 to April 2014, 87 women (median age: 58; median 5 prior lines of antineoplastic therapy) received escalating doses of alpelisib QD (300 mg, n=9; 350 mg, n=8; 400-mg, n=70) plus fixed-dose fulvestrant (500 mg). During dose-escalation, dose-limiting toxicities were reported in 1 patient (alpelisib 400 mg): diarrhea (grade 2), vomiting, fatigue, and decreased appetite (all grade 3). The MTD of alpelisib (plus fulvestrant) was 400 mg QD, and the recommended phase 2 dose was 300 mg QD. Overall, the most frequent grade 3/4 adverse events with alpelisib 400 mg QD (≥10% of patients), regardless of causality, were hyperglycemia (22%) and maculopapular rash (13%); 9 patients permanently discontinued due to adverse events. Median progression-free survival (mPFS) at the MTD was 5.4 months (95% CI: 4.6–9.0). mPFS of alpelisib 300–400 mg QD plus fulvestrant was longer in patients with PIK3CA-altered tumors (9.1 months [95% CI: 6.6–14.6]) vs wild-type tumors (4.7 months [95% CI: 1.9–5.6]). Overall response rate in the PIK3CA-altered group was 29%, with no objective tumor responses in the wild-type group. Conclusions and relevance: Alpelisib plus fulvestrant has a manageable safety profile in patients with ER+ ABC, and data suggest that this combination may have greater clinical activity in PIK3CA-altered vs wild-type tumors

A Phase 1b/2 Study of Alpelisib in Combination with Cetuximab in Patients with Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma.

Alpelisib in combination with cetuximab showed synergistic anti-tumour activity in head and neck squamous cell carcinoma (HNSCC) models.The recommended phase 2 dose (RP2D) was determined in a phase 1b dose-escalation study. Phase 2 evaluated anti-tumour activity with a randomised part in cetuximab-naïve patients and a non-randomised part in cetuximab-resistant patients. Alpelisib was administered in 28 d cycles as whole tablets, suspension from crushed tablets or suspension from dispersible tablets in patients with platinum-resistant, recurrent/metastatic HNSCC.The RP2D determined for alpelisib was 300 mg/d. Alpelisib-cetuximab achieved an overall response rate of 25% and 9.9% and disease control rate of 75% and 43.7% in phase 1b and phase 2 studies, respectively. Median progression-free survival (PFS) per central review was 86 d for combination treatment and 87 d for cetuximab monotherapy (unadjusted HR 1.12; 95% CI 0.69-1.82; P > 0.05). When adjusted for baseline covariates [sum of longest diameters from central data, haemoglobin and white blood cell (WBC), the results favoured combination treatment (adjusted HR 0.54; 95% CI 0.30-0.97; P = 0.039). PFS per investigator assessment resulted in an unadjusted HR of 0.76 (95% CI 0.49-1.19; P > 0.05) favouring combination treatment. The median PFS in cetuximab-resistant patients was 3.9 months.The addition of alpelisib to cetuximab did not demonstrate a PFS benefit in cetuximab-naïve patients with advanced HNSCC. The alpelisib-cetuximab combination showed moderate activity in cetuximab-resistant patients, with a consistent safety profile.ClinicalTrials.gov NCT01602315; EudraCT 2011-006017-34

A Phase Ib Dose-Escalation Study of the Oral Pan-PI3K Inhibitor Buparlisib (BKM120) in Combination with the Oral MEK1/2 Inhibitor Trametinib (GSK1120212) in Patients with Selected Advanced Solid Tumors

PURPOSE: MAPK and PI3K/AKT/mTOR pathways play important roles in many tumors. In this study, safety, antitumor activity, and pharmacokinetics of buparlisib (pan class PI3K inhibitor) and trametinib (MEK inhibitor) were evaluated. EXPERIMENTAL DESIGN: This open-label, dose-finding, phase Ib study comprised dose escalation, followed by expansion part in patients with RAS- or BRAF-mutant non-small cell lung, ovarian, or pancreatic cancer. RESULTS: Of note, 113 patients were enrolled, 66 and 47 in dose-escalation and -expansion parts, respectively. MTD was established as buparlisib 70 mg + trametinib 1.5 mg daily [5/15, 33% patients with dose-limiting toxicities (DLT)] and recommended phase II dose (RP2D) buparlisib 60 mg + trametinib 1.5 mg daily (1/10, 10% patients with DLTs). DLTs included stomatitis (8/103, 8%), diarrhea, dysphagia, and creatine kinase (CK) increase (2/103, 2% each). Treatment-related grade 3/4 adverse events (AEs) occurred in 73 patients (65%); mainly CK increase, stomatitis, AST/ALT (aspartate aminotransferase/alanine aminotransferase) increase, and rash. For all (21) patients with ovarian cancer, overall response rate was 29% [1 complete response, 5 partial responses (PR)], disease control rate 76%, and median progression-free survival was 7 months. Minimal activity was observed in patients with non-small cell lung cancer (1/17 PR) and pancreatic cancer (best overall response was SD). Relative to historical data, buparlisib exposure increased and trametinib exposure slightly increased with the combination. CONCLUSIONS: At RP2D, buparlisib 60 mg + trametinib 1.5 mg daily shows promising antitumor activity for patients with KRAS-mutant ovarian cancer. Long-term tolerability of the combination at RP2D is challenging, due to frequent dose interruptions and reductions for toxicity.status: publishe