Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is associated with a low survival rate at advanced stages. Although the development of targeted therapies has improved outcomes in LUAD patients with identified and specific genetic alterations, such as activating mutations on the epidermal growth factor receptor gene (EGFR), the emergence of tumor resistance eventually occurs in all patients and this is driving the development of new therapies. In this paper, we present the In Silico EGFR-mutant LUAD (ISELA) model that links LUAD patients' individual characteristics, including tumor genetic heterogeneity, to tumor size evolution and tumor progression over time under first generation EGFR tyrosine kinase inhibitor gefitinib. This translational mechanistic model gathers extensive knowledge on LUAD and was calibrated on multiple scales, including in vitro, human tumor xenograft mouse and human, reproducing more than 90% of the experimental data identified. Moreover, with 98.5% coverage and 99.4% negative logrank tests, the model accurately reproduced the time to progression from the Lux-Lung 7 clinical trial, which was unused in calibration, thus supporting the model high predictive value. This knowledge-based mechanistic model could be a valuable tool in the development of new therapies targeting EGFR-mutant LUAD as a foundation for the generation of synthetic control arms.

Predictive validity in drug discovery: what it is, why it matters and how to improve it.

Successful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which 'predictive validity' is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation.

Seasonality and prevalence of respiratory pathogens detected by multiplex PCR at a tertiary care medical center.

Respiratory tract infections (RTIs) are a leading cause of mortality and morbidity. Seasonality has been reported for many viruses, including influenza virus, respiratory syncytial virus (RSV), and the recently described human metapneumovirus (hMPV). We hypothesize that the availability of rapid, multiplex PCR diagnostics will provide better clinical care and new insights into the etiology and clinical spectrum of RTIs. We conducted a retrospective analysis of the incidence of respiratory pathogens at a 500-bed adult and 154-bed pediatric hospital tertiary care center. A total of 939 specimens from patients with an age range of 5 days to 91 years (median, 2 years) were tested by a multiplex respiratory pathogen PCR from November 14, 2011 to November 13, 2012. Sixty-five percent of specimens were positive for at least one pathogen. As the age of the patient increased, the positivity rate for the PCR decreased proportionately. Rhinoviruses/enteroviruses (Rhino/Entero) were the most prevalent (34.3 %) followed by RSV (19.2 %) and hMPV (6.2 %). Twelve percent of the positive samples were positive for multiple analytes, with Rhino/Entero and RSV being the most common combination. The peak months were September and May for Rhino/Entero infections, January for RSV and February for coronavirus. hMPV peaked 2 months after RSV, as has been observed recently in other studies. Multiplex PCR provides rapid diagnostic information that can be used to make knowledgeable clinical decisions and potentially reduce the use of antibiotics. Active respiratory PCR surveillance could also predict seasonal respiratory epidemics to allow for adequate planning of additional infection control measures.