Phase Ia study of the indoleamine 2,3-dioxygenase 1 (IDO1) inhibitor navoximod (GDC-0919) in patients with recurrent advanced solid tumors.

BACKGROUND: Indoleamine-2,3-dioxygenase 1 (IDO1) catalyzes the oxidation of tryptophan into kynurenine and is partially responsible for acquired immune tolerance associated with cancer. The IDO1 small molecule inhibitor navoximod (GDC-0919, NLG-919) is active as a combination therapy in multiple tumor models. METHODS: This open-label Phase Ia study assessed safety, pharmacokinetics (PK), pharmacodynamics (PD), and preliminary anti-tumor activity of navoximod in patients with recurrent/advanced solid tumors, administered as 50-800 mg BID on a 21/28 day and at 600 mg on a 28/28 day schedule. Plasma kynurenine and tryptophan were longitudinally evaluated and tumor assessments were performed. RESULTS: Patients (n = 22) received a median of 3 cycles of navoximod. No maximum tolerated dose was reached. One dose-limiting toxicity of Grade 4 lower gastrointestinal hemorrhage was reported. Adverse events (AEs) regardless of causality in ≥20% of patients included fatigue (59%), cough, decreased appetite, and pruritus (41% each), nausea (36%), and vomiting (27%). Grade ≥ 3 AEs occurred in 14/22 patients (64%), and were related to navoximod in two patients (9%). Navoximod was rapidly absorbed (Tmax ~ 1 h) and exhibited dose-proportional increases in exposure, with a half-life (t1/2 ~ 11 h) supportive of BID dosing. Navoximod transiently decreased plasma kynurenine from baseline levels with kinetics consistent with its half-life. Of efficacy-evaluable patients, 8 (36%) had stable disease and 10 (46%) had progressive disease. CONCLUSIONS: Navoximod was well-tolerated at doses up to 800 mg BID decreasing plasma kynurenine levels consistent with its half-life. Stable disease responses were observed. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02048709 .

Phase I Dose-Escalation Study of Taselisib, an Oral PI3K Inhibitor, in Patients with Advanced Solid Tumors.

Taselisib is a potent and selective tumor growth inhibitor through PI3K pathway suppression. Thirty-four patients with locally advanced or metastatic solid tumors were treated (phase I study, modified 3+3 dose escalation; 5 cohorts; 3-16 mg taselisib once-daily capsule). Taselisib pharmacokinetics were dose-proportional; mean half-life was 40 hours. Frequent dose-dependent, treatment-related adverse events included diarrhea, hyperglycemia, decreased appetite, nausea, rash, stomatitis, and vomiting. At 12 and 16 mg dose levels, dose-limiting toxicities (DLT) were observed, with an accumulation of higher-grade adverse events after the cycle 1 DLT assessment window. Pharmacodynamic findings showed pathway inhibition at ≥3 mg in patient tumor samples, consistent with preclinical PIK3CA-mutant tumor xenograft models. Confirmed response rate was 36% for PIK3CA-mutant tumor patients with measurable disease [5/14: 4 breast cancer (3 patients at 12 mg); 1 non-small cell lung cancer], where responses started at 3 mg, and 0% in patients with tumors without known PIK3CA hotspot mutations (0/15).Significance: Preliminary data consistent with preclinical data indicate increased antitumor activity of taselisib in patients with PIK3CA-mutant tumors (in comparison with patients with tumors without known activating PIK3CA hotspot mutations) starting at the lowest dose tested of 3 mg, thereby supporting higher potency for taselisib against PIK3CA-mutant tumors. Cancer Discov; 7(7); 704-15. ©2017 AACR.See related commentary by Rodon and Tabernero, p. 666This article is highlighted in the In This Issue feature, p. 653.

Estimation of treatment effects in weighted log-rank tests.

Non-proportional hazards have been observed in clinical trials. The log-rank test loses power and the standard Cox model generally produces biased estimates under such conditions. Weighted log-rank tests have been utilized to increase the test power; however, it is not intuitive how to interpret the test result in terms of the clinical effect. We propose a Cox-model based time-varying treatment effect estimate to complement the weighted log-rank test. The score test from the proposed model is equivalent to the weighted log-rank test, and a time-profile of the treatment effect can be obtained by fitting a time-varying covariate Cox model. Simulation results show that the proposed model preserves type-I error and achieve higher power than log-rank tests under non-proportional hazards scenarios. Whereas the standard Cox model produces biased effect estimates, the proposed model produces unbiased estimates if the weight function is correctly specified. It also achieves a better model fit and an enhanced flexibility to accommodate non-proportional hazards compared to the standard Cox model. The proposed approach makes the assumptions of the weighted log-rank test explicit and the validity of assumptions can be assessed based on prior knowledge or model goodness-of-fit. It also helps to translate the weighted log-rank test results into quantitative estimates of the treatment effect with intuitive interpretation. The proposed method can be routinely conducted to complement weighted log-rank tests, especially in the setting where non-proportional hazards are expected.

Novel Noxipoint Therapy versus Conventional Physical Therapy for Chronic Neck and Shoulder Pain: Multicentre Randomised Controlled Trials.

As chronic pain affects 115 million people and costs $600B annually in the US alone, effective noninvasive nonpharmacological remedies are desirable. The purpose of this study was to determine the efficacy and the generalisability of Noxipoint therapy (NT), a novel electrotherapy characterised by site-specific stimulation, intensity-and-submodality-specific settings and a immobilization period, for chronic neck and shoulder pain. Ninety-seven heavily pretreated severe chronic neck/shoulder pain patients were recruited; 34 and 44 patients were randomly allocated to different treatment arms in two patient-and-assessor-blinded, randomised controlled studies. The participants received NT or conventional physical therapy including transcutaneous electrical nerve stimulation (PT-TENS) for three to six 90-minute sessions. In Study One, NT improved chronic pain (-89.6%, Brief Pain Inventory, p < 0.0001, 95% confidence interval), function (+77.4%, range of motion) and quality of life (+88.1%) at follow-up (from 4 weeks to 5 months), whereas PT-TENS resulted in no significant changes in these parameters. Study Two demonstrated similar advantages of NT over PT-TENS and the generalisability of NT. NT-like treatments in a randomised rat study showed a similar reduction in chronic hypersensitivity (-81%, p < 0.01) compared with sham treatments. NT substantially reduces chronic neck and shoulder pain, restores function, and improves quality of life in a sustained manner.

Comparing the benefits of screening for breast cancer and lung cancer using a novel natural history model.

To estimate the impact of early detection of cancer, knowledge of how quickly primary tumors grow and at what size they shed lethal metastases is critical. We developed a natural history model of cancer to estimate the probability of disease-specific cure as a function of tumor size, the tumor volume doubling time (TVDT), and disease-specific mortality reduction achievable by screening. The model was applied to non-small-cell lung carcinoma (NSCLC) and invasive ductal carcinoma (IDC), separately. Model parameter estimates were based on Surveillance Epidemiology and End Results (SEER) cancer registry datasets and validated on screening trials. Compared to IDC, NSCLC is estimated to have a lower probability of disease-specific cure at the same detected tumor size, shed lethal metastases at smaller sizes (median: 19 mm for IDC versus 8 mm for NSCLC), have a TVDT that is almost half as long (median: 252 days for IDC versus 134 days for NSCLC). Consequently, NSCLC is associated with a lower mortality reduction from screening at the same screen detection threshold and screening interval. In summary, using a similar natural history model of cancer, we quantify the disease-specific curability attributable to screening for breast cancer, and separately lung cancer, in terms of the TVDT and onset of lethal metastases.

Single and multiple time-point prediction models in kidney transplant outcomes.

This study predicted graft and recipient survival in kidney transplantation based on the USRDS dataset by regression models and artificial neural networks (ANNs). We examined single time-point models (logistic regression and single-output ANNs) versus multiple time-point models (Cox models and multiple-output ANNs). These models in general achieved good prediction discrimination (AUC up to 0.82) and model calibration. This study found that: (1) Single time-point and multiple time-point models can achieve comparable AUC, except for multiple-output ANNs, which may perform poorly when a large proportion of observations are censored, (2) Logistic regression is able to achieve comparable performance as ANNs if there are no strong interactions or non-linear relationships among the predictors and the outcomes, (3) Time-varying effects must be modeled explicitly in Cox models when predictors have significantly different effects on short-term versus long-term survival, and (4) Appropriate baseline survivor function should be specified for Cox models to achieve good model calibration, especially when clinical decision support is designed to provide exact predicted survival rates.

A combined data mining approach for infrequent events: analyzing HIV mutation changes based on treatment history.

Many biological databases contain a large number of variables, among which events of interest may be very infrequent. Using a single data mining method to analyze such databases may not find adequate predictors. The HIV Drug Resistance Database at Stanford University stores sequential HIV-1 genotype-test results on patients taking antiretroviral drugs. We have analyzed the infrequent event of gene mutation changes by combining three data mining methods. We first use association rule analysis to scan through the database and identify potentially interesting mutation patterns with relatively high frequency. Next, we use logistic regression and classification trees to further investigate these patterns by analyzing the relationship between treatment history and mutation changes. Although the AUC measures of the overall prediction is not very high, our approach can effectively identify strong predictors of mutation change and thus focus the analytic efforts of researchers in verifying these results.

Prediction of HIV mutation changes based on treatment history.

Few studies have investigated sequential HIV-1 mutation changes in the HIV gene in patients changing antiretroviral drugs. We analyze such data from the HIV Drug Resistance Database at Stanford University using three data mining methods: association rule analysis, logistic regression, and classification trees. Although the AUC measures of the overall prediction is not high, these methods can effectively identify strong predictors of mutation change and focus further analysis by domain experts.