Challenges and Opportunities to Updating Prescribing Information for Longstanding Oncology Drugs.

A number of important drugs used to treat cancer-many of which serve as the backbone of modern chemotherapy regimens-have outdated prescribing information in their drug labeling. The Food and Drug Administration is undertaking a pilot project to develop a process and criteria for updating prescribing information for longstanding oncology drugs, based on the breadth of knowledge the cancer community has accumulated with the use of these drugs over time. This article highlights a number of considerations for labeling updates, including selecting priorities for updating; data sources and evidentiary criteria; as well as the risks, challenges, and opportunities for iterative review to ensure prescribing information for oncology drugs remains relevant to current clinical practice

Phase Ib/II Study of the Safety and Efficacy of Combination Therapy with Multikinase VEGF Inhibitor Pazopanib and MEK Inhibitor Trametinib In Advanced Soft Tissue Sarcoma.

Purpose: Pazopanib, a multireceptor tyrosine kinase inhibitor targeting primarily VEGFRs1–3, is approved for advanced soft tissue sarcoma (STS) and renal cell cancer. Downstream of VEGFR, trametinib is an FDA-approved MEK inhibitor used for melanoma. We hypothesized that vertical pathway inhibition using trametinib would synergize with pazopanib in advanced STS. Experimental Design: In an open-label, multicenter, investigator-initiated National Comprehensive Cancer Network (NCCN)-sponsored trial, patients with metastatic or advanced STS received pazopanib 800 mg and 2 mg of trametinib continuously for 28-day cycles. The primary endpoint was 4-month progression-free survival (PFS). Secondary endpoints were overall survival, response rate, and disease control rate. Results: Twenty-five patients were enrolled. The median age was 49 years (range, 22–77 years) and 52% were male. Median PFS was 2.27 months [95% confidence interval (CI), 1.9–3.9], and the 4-month PFS rate was 21.1% (95% CI, 9.7–45.9), which was not an improvement over the hypothesized null 4-month PFS rate of 28.3% (P ¼ 0.79). Median overall survival was 9.0 months (95% CI, 5.7–17.7). A partial response occurred in 2 (8%) of the evaluable patients (95% CI, 1.0–26.0), one with PIK3CA E542K-mutant embryonal rhabdomyosarcoma and another with spindle cell sarcoma. The disease control rate was 14/25 (56%; 95% CI, 34.9–75.6). The most common adverse events were diarrhea (84%), nausea (64%), and fatigue (56%). Conclusions: The combination of pazopanib and trametinib was tolerable without indication of added activity of the combination in STS. Further study may be warranted in RAS/RAF aberrant sarcomas. ©2017 AACR

Efficacy and safety of bortezomib with dexamethasone regimen in elderly newly diagnosed multiple myeloma patients with co-morbidities

Bortezomib-based induction therapies have shown to increase complete response rates and are used as an upfront therapy for newly diagnosed multiple myeloma patients. The standard treatment uses twice a week bortezomib at 1.3 mg/m^2 with dexamethasone PO on the day of and day after bortezomib, however, peripheral neuropathy is often a dose-limiting factor. For elderly patients with multiple co-morbidities and polypharmacy, we propose an alternate schedule of once a week bortezomib IV at 1.6 mg/m^2 with dexamethasone PO on the day of and day after bortezomib. In this phase II, open-labeled, multi-site study, we hypothesize that patients receiving weekly bortezomib will have comparable efficacy as the standard twice a week schedule with increased convenience and lower toxicity profile, especially related to peripheral neuropathy. METHODS: 50 patients with newly diagnosed symptomatic multiple myeloma who were ineligible for transplant or postponed transplant were enrolled from 12 Veterans Affairs hospitals. One cycle consisted of once a week 1.6 mg/m2 bortezomib IV (days 1, 8, 15, 22) plus dexamethasone PO on the day of and after bortezomib (days 1, 2, 8, 9, 15, 16, 22, 23) for 4 weeks, with the 5th week off of treatment. Responding patients could receive up to 6 cycles. RESULTS: The median age of patients was 71 ± 1.46 years (range: 50-89) with β-2 microglobulin of 5.80 ± 0.46 mg/L and c-reactive protein of 10.61 ± 5.54 mg/L. Patients also had multiple co-morbidities including cardiovascular disease (76%) renal insufficiency (54%) and pulmonary problems (36%) and were receiving a median of 13 concurrent medications at baseline. Of the fifty patients, 43 patients were evaluable for response. Seven patients received <1 cycle or died before response could be evaluated. An objective response rate of 79% was observed in 43 evaluable patients with 14% achieving nCR/CR, and at least VGPR in 44% of patients. The median progression-free survival was 9.6 months and overall survival was 46.5 months. The most common toxicity of all grades was thrombocytopenia (42%), lymphopenia (46%), asthenia (48%), and constipation (38%). Peripheral neuropathy occurred in 24% with grade 3 neuropathy occurring only in 6% of patients. In conclusion, a weekly bortezomib plus dexamethasone regimen is efficacious and safe, with lower neurotoxicity in elderly patients with newly diagnosed multiple myeloma complicated by extensive co-morbidities and polypharmacy

Prophylactic antibiotic regimens in tumour surgery (PARITY): protocol for a multicentre randomised controlled study.

IntroductionLimb salvage with endoprosthetic reconstruction is the standard of care for the management of lower-extremity bone tumours in skeletally mature patients. The risk of deep postoperative infection in these procedures is high and the outcomes can be devastating. The most effective prophylactic antibiotic regimen remains unknown, and current clinical practice is highly varied. This trial will evaluate the effect of varying postoperative prophylactic antibiotic regimens on the incidence of deep infection following surgical excision and endoprosthetic reconstruction of lower-extremity bone tumours.Methods and analysisThis is a multicentre, blinded, randomised controlled trial, using a parallel two-arm design. 920 patients 15 years of age or older from 12 tertiary care centres across Canada and the USA who are undergoing surgical excision and endoprosthetic reconstruction of a primary bone tumour will receive either short (24 h) or long (5 days) duration postoperative antibiotics. Exclusion criteria include prior surgery or infection within the planned operative field, known colonisation with methicillin-resistant Staphylococcus aureus or vancomycin-resistant Enterococcus at enrolment, or allergy to the study antibiotics. The primary outcome will be rates of deep postoperative infections in each arm. Secondary outcomes will include type and frequency of antibiotic-related adverse events, patient functional outcomes and quality-of-life scores, reoperation and mortality. Randomisation will be blocked, with block sizes known only to the methods centre responsible for randomisation, and stratified by location of tumour and study centre. Patients, care givers and a Central Adjudication Committee will be blinded to treatment allocation. The analysis to compare groups will be performed using Cox regression and log-rank tests to compare survival functions at α=0.05.Ethics and disseminationThis study has ethics approval from the McMaster University/Hamilton Health Sciences Research Ethics Board (REB# 12-009). Successful completion will significantly impact on clinical practice and enhance patients' lives. More broadly, this trial will develop a network of collaboration from which further high-quality trials in Orthopaedic Oncology will follow

Targeted therapy of advanced gallbladder cancer and cholangiocarcinoma with aggressive biology: eliciting early response signals from phase 1 trials.

PurposePatients with advanced cholangiocarcinoma (CC) and gallbladder carcinoma (GC) have few therapeutic options for relapsed disease. methods: Given the overall poor prognosis in this population and the availability of novel targeted therapies, we systematically analyzed the characteristics and outcomes for GC and CC patients treated on phase I trials with an emphasis on targeted agents and locoregional therapies.ResultsOf 40 treated patients (GC=6; CC=34; median age, 60 years), 8 (20%) had stable disease (SD) &gt; 6 months, 3 (8%) partial response (PR), on protocols with hepatic arterial drug infusion and anti-angiogenic, anti-HER-2/neu or novel MAPK/ERK kinase (MEK) inhibitors. Median progression-free survival (PFS) on phase I trials was 2.0 months (95% CI 1.7, 2.8) versus 3.0 months (95% CI 2.4, 5.0), 3.0 months (95% CI 2.3, 4.6), and 3.0 months (95% CI 2.4, 3.9) for their first-, second-, and last-line FDA-approved therapy. In univariate analysis, &gt;3 metastatic sites, elevated alanine aminotransferase (ALT) (&gt;56IU/L), serum creatinine (&gt;1.6mg/dL), and CA19-9 (&gt;35U/mL) were associated with a shorter PFS. Mutational analysis revealed mutation in the KRAS oncogene in 2 of 11 patients (18%). The SD &gt;6 months/PR rate of 28% was seen with hepatic arterial infusion of oxaliplatin, and inhibitors of angiogenesis, HER-2/neu or MEK.ConclusionsThe PFS in phase I trials was similar to that of the first, second, and last-line therapy (P=0.95, 0.98, 0.76, respectively) with FDA-approved agents given in the advanced setting, emphasizing a role for targeted agents in a clinical trials setting as potentially valuable therapeutic options for these patients

Three Essays on Enhancing Clinical Trial Subject Recruitment Using Natural Language Processing and Text Mining

Patient recruitment and enrollment are critical factors for a successful clinical trial; however, recruitment tends to be the most common problem in most clinical trials. The success of a clinical trial depends on efficiently recruiting suitable patients to conduct the trial. Every clinical trial research has a protocol, which describes what will be done in the study and how it will be conducted. Also, the protocol ensures the safety of the trial subjects and the integrity of the data collected. The eligibility criteria section of clinical trial protocols is important because it specifies the necessary conditions that participants have to satisfy. Since clinical trial eligibility criteria are usually written in free text form, they are not computer interpretable. To automate the analysis of the eligibility criteria, it is therefore necessary to transform those criteria into a computer-interpretable format. Unstructured format of eligibility criteria additionally create search efficiency issues. Thus, searching and selecting appropriate clinical trials for a patient from relatively large number of available trials is a complex task. A few attempts have been made to automate the matching process between patients and clinical trials. However, those attempts have not fully integrated the entire matching process and have not exploited the state-of-the-art Natural Language Processing (NLP) techniques that may improve the matching performance. Given the importance of patient recruitment in clinical trial research, the objective of this research is to automate the matching process using NLP and text mining techniques and, thereby, improve the efficiency and effectiveness of the recruitment process. This dissertation research, which comprises three essays, investigates the issues of clinical trial subject recruitment using state-of-the-art NLP and text mining techniques. Essay 1: Building a Domain-Specific Lexicon for Clinical Trial Subject Eligibility Analysis Essay 2: Clustering Clinical Trials Using Semantic-Based Feature Expansion Essay 3: An Automatic Matching Process of Clinical Trial Subject Recruitment In essay1, I develop a domain-specific lexicon for n-gram Named Entity Recognition (NER) in the breast cancer domain. The domain-specific dictionary is used for selection and reduction of n-gram features in clustering in eassy2. The domain-specific dictionary was evaluated by comparing it with Systematized Nomenclature of Medicine--Clinical Terms (SNOMED CT). The results showed that it add significant number of new terms which is very useful in effective natural language processing In essay 2, I explore the clustering of similar clinical trials using the domain-specific lexicon and term expansion using synonym from the Unified Medical Language System (UMLS). I generate word n-gram features and modify the features with the domain-specific dictionary matching process. In order to resolve semantic ambiguity, a semantic-based feature expansion technique using UMLS is applied. A hierarchical agglomerative clustering algorithm is used to generate clinical trial clusters. The focus is on summarization of clinical trial information in order to enhance trial search efficiency. Finally, in essay 3, I investigate an automatic matching process of clinical trial clusters and patient medical records. The patient records collected from a prior study were used to test our approach. The patient records were pre-processed by tokenization and lemmatization. The pre-processed patient information were then further enhanced by matching with breast cancer custom dictionary described in essay 1 and semantic feature expansion using UMLS Metathesaurus. Finally, I matched the patient record with clinical trial clusters to select the best matched cluster(s) and then with trials within the clusters. The matching results were evaluated by internal expert as well as external medical expert