The amount of data in our world has been exploding, and analyzing large data sets—so-called big data— will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey's Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.

Big data: The next frontier for innovation, competition, and productivity

The efficacy of therapeutics is dependent on a drug binding to its cognate target. Optimization of target engagement by drugs in cells is often challenging, because drug binding cannot be monitored inside cells. We have developed a method for evaluating drug binding to target proteins in cells and tissue samples. This cellular thermal shift assay (CETSA) is based on the biophysical principle of ligand-induced thermal stabilization of target proteins. Using this assay, we validated drug binding for a set of important clinical targets and monitored processes of drug transport and activation, off-target effects and drug resistance in cancer cell lines, as well as drug distribution in tissues. CETSA is likely to become a valuable tool for the validation and optimization of drug target engagement.

http://science.sciencemag.org/content/sci/341/6141/84.full.pdf

Monitoring Drug Target Engagement in Cells and Tissues Using the Cellular Thermal Shift Assay

Informative diagnostic tools are vital to the development of useful mixed-effects models. The Visual Predictive Check (VPC) is a popular tool for evaluating the performance of population PK and PKPD models. Ideally, a VPC will diagnose both the fixed and random effects in a mixed-effects model. In many cases, this can be done by comparing different percentiles of the observed data to percentiles of simulated data, generally grouped together within bins of an independent variable. However, the diagnostic value of a VPC can be hampered by binning across a large variability in dose and/or influential covariates. VPCs can also be misleading if applied to data following adaptive designs such as dose adjustments. The prediction-corrected VPC (pcVPC) offers a solution to these problems while retaining the visual interpretation of the traditional VPC. In a pcVPC, the variability coming from binning across independent variables is removed by normalizing the observed and simulated dependent variable based on the typical population prediction for the median independent variable in the bin. The principal benefit with the pcVPC has been explored by application to both simulated and real examples of PK and PKPD models. The investigated examples demonstrate that pcVPCs have an enhanced ability to diagnose model misspecification especially with respect to random effects models in a range of situations. The pcVPC was in contrast to traditional VPCs shown to be readily applicable to data from studies with a priori and/or a posteriori dose adaptations.

Prediction-Corrected Visual Predictive Checks for Diagnosing Nonlinear Mixed-Effects Models

CONTEXT: Daily left prefrontal repetitive transcranial magnetic stimulation (rTMS) has been studied as a potential treatment for depression, but previous work had mixed outcomes and did not adequately mask sham conditions. OBJECTIVE: To test whether daily left prefrontal rTMS safely and effectively treats major depressive disorder. DESIGN: Prospective, multisite, randomized, active sham-controlled (1:1 randomization), duration-adaptive design with 3 weeks of daily weekday treatment (fixed-dose phase) followed by continued blinded treatment for up to another 3 weeks in improvers. SETTING: Four US university hospital clinics. PATIENTS: Approximately 860 outpatients were screened, yielding 199 antidepressant drug-free patients with unipolar nonpsychotic major depressive disorder. INTERVENTION: We delivered rTMS to the left prefrontal cortex at 120% motor threshold (10 Hz, 4-second train duration, and 26-second intertrain interval) for 37.5 minutes (3000 pulses per session) using a figure-eight solid-core coil. Sham rTMS used a similar coil with a metal insert blocking the magnetic field and scalp electrodes that delivered matched somatosensory sensations. MAIN OUTCOME MEASURE: In the intention-to-treat sample (n = 190), remission rates were compared for the 2 treatment arms using logistic regression and controlling for site, treatment resistance, age, and duration of the current depressive episode. RESULTS: Patients, treaters, and raters were effectively masked. Minimal adverse effects did not differ by treatment arm, with an 88% retention rate (90% sham and 86% active). Primary efficacy analysis revealed a significant effect of treatment on the proportion of remitters (14.1% active rTMS and 5.1% sham) (P = .02). The odds of attaining remission were 4.2 times greater with active rTMS than with sham (95% confidence interval, 1.32-13.24). The number needed to treat was 12. Most remitters had low antidepressant treatment resistance. Almost 30% of patients remitted in the open-label follow-up (30.2% originally active and 29.6% sham). CONCLUSION: Daily left prefrontal rTMS as monotherapy produced statistically significant and clinically meaningful antidepressant therapeutic effects greater than sham. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00149838. Language: en

https://jamanetwork.com/journals/jamapsychiatry/articlepdf/210744/yoa05003_507_516.pdf

Daily left prefrontal transcranial magnetic stimulation therapy for major depressive disorder: a sham-controlled randomized trial

CONTEXT
Daily left prefrontal repetitive transcranial magnetic stimulation (rTMS) has been studied as a potential treatment for depression, but previous work had mixed outcomes and did not adequately mask sham conditions.


OBJECTIVE
To test whether daily left prefrontal rTMS safely and effectively treats major depressive disorder.


DESIGN
Prospective, multisite, randomized, active sham-controlled (1:1 randomization), duration-adaptive design with 3 weeks of daily weekday treatment (fixed-dose phase) followed by continued blinded treatment for up to another 3 weeks in improvers.


SETTING
Four US university hospital clinics.


PATIENTS
Approximately 860 outpatients were screened, yielding 199 antidepressant drug-free patients with unipolar nonpsychotic major depressive disorder.


INTERVENTION
We delivered rTMS to the left prefrontal cortex at 120% motor threshold (10 Hz, 4-second train duration, and 26-second intertrain interval) for 37.5 minutes (3000 pulses per session) using a figure-eight solid-core coil. Sham rTMS used a similar coil with a metal insert blocking the magnetic field and scalp electrodes that delivered matched somatosensory sensations.


MAIN OUTCOME MEASURE
In the intention-to-treat sample (n = 190), remission rates were compared for the 2 treatment arms using logistic regression and controlling for site, treatment resistance, age, and duration of the current depressive episode.


RESULTS
Patients, treaters, and raters were effectively masked. Minimal adverse effects did not differ by treatment arm, with an 88% retention rate (90% sham and 86% active). Primary efficacy analysis revealed a significant effect of treatment on the proportion of remitters (14.1% active rTMS and 5.1% sham) (P = .02). The odds of attaining remission were 4.2 times greater with active rTMS than with sham (95% confidence interval, 1.32-13.24). The number needed to treat was 12. Most remitters had low antidepressant treatment resistance. Almost 30% of patients remitted in the open-label follow-up (30.2% originally active and 29.6% sham).


CONCLUSION
Daily left prefrontal rTMS as monotherapy produced statistically significant and clinically meaningful antidepressant therapeutic effects greater than sham.


TRIAL REGISTRATION
clinicaltrials.gov Identifier: NCT00149838.

http://tmschattanooga.com/wp-content/uploads/2012/01/Daily-Left-Prefrontal-TMS-Therapy-for-Major-Depressive-Disorder.pdf

Daily Left Prefrontal Transcranial Magnetic Stimulation Therapy for Major Depressive Disorder

'Personalized medicine' promises to increase the quality of clinical care and, in some cases, decrease health-care costs. Despite this, only a handful of diagnostic tests have made it to market, with mixed success. Historically, the challenges in this field were scientific. However, as discussed in this article, with the maturation of the '-omics' sciences, it now seems that the major barriers are increasingly related to economics. Overcoming the poor microeconomic alignment of incentives among key stakeholders is therefore crucial to catalysing the further development and adoption of personalized medicine, and we propose several actions that could help achieve this goal.

The microeconomics of personalized medicine: today's challenge and tomorrow's promise

Declining pharmaceutical industry productivity is well recognized by drug developers, regulatory authorities and patient groups. A key part of the problem is that clinical studies are increasingly expensive, driven by the rising costs of conducting Phase II and III trials. It is therefore crucial to ensure that these phases of drug development are conducted more efficiently and cost-effectively, and that attrition rates are reduced. In this article, we argue that moving from the traditional clinical development approach based on sequential, distinct phases towards a more integrated view that uses adaptive design tools to increase flexibility and maximize the use of accumulated knowledge could have an important role in achieving these goals. Applications and examples of the use of these tools--such as Bayesian methodologies--in early- and late-stage drug development are discussed, as well as the advantages, challenges and barriers to their more widespread implementation.

The future of drug development: advancing clinical trial design

Specialty pharmaceuticals have become increasingly important in the global pharmaceutical landscape. Numerous large pharmaceutical companies are moving towards developing therapies for specialty markets, which are attractive owing to factors including the established commercial track record and lower commercial infrastructure costs. In this article, we analyse the key drivers of commercial success and failure for specialty pharmaceuticals.

What drives success for specialty pharmaceuticals

Drug life-cycle technologies.

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The microeconomics of personalized medicine: today's challenge and tomorrow's promise

The future of drug development: advancing clinical trial design

What drives success for specialty pharmaceuticals

Drug life-cycle technologies.