Concept: Drug development
Options for leveraging available telemedicine technologies, ranging from simple webcams and telephones to smartphone apps and medical-grade wearable sensors, are evolving faster than the culture of clinical research. Until recently, most clinical trials relied on paper-based processes and technology. This cost- and labor-intensive system, while slowly changing, remains an obstacle to new drug development. Alternatives that use existing tools and processes for collecting real-world data in home settings warrant closer examination.
- Journal of clinical oncology : official journal of the American Society of Clinical Oncology
- Published about 3 years ago
The use of biopsy-derived pharmacodynamic biomarkers is increasing in early-phase clinical trials. It remains unknown whether drug development is accelerated or enhanced by their use. We examined the impact of biopsy-derived pharmacodynamic biomarkers on subsequent drug development through a comprehensive analysis of phase I oncology studies from 2003 to 2010 and subsequent publications citing the original trials.
Three dedicated approaches to the calculation of the risk-adjusted net present value (rNPV) in drug discovery projects under different assumptions are suggested. The probability of finding a candidate drug suitable for clinical development and the time to the initiation of the clinical development is assumed to be flexible in contrast to the previously used models. The rNPV of the post-discovery cash flows is calculated as the probability weighted average of the rNPV at each potential time of initiation of clinical development. Practical considerations how to set probability rates, in particular during the initiation and termination of a project is discussed.
The American College of Physicians (ACP) and the American Academy of Family Physicians (AAFP) jointly developed this guideline to present the evidence and provide clinical recommendations based on the benefits and harms of higher versus lower blood pressure targets for the treatment of hypertension in adults aged 60 years or older.
Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7 and PC-3 cell lines from the LINCS project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled dataset of samples perturbed with different concentrations of the drug for 6 and 24 hours. When applied to normalized gene expression data for “landmark genes,” DNN showed cross-validation mean F1 scores of 0.397, 0.285 and 0.234 on 3-, 5- and 12-category classification problems, respectively. At the pathway level DNN performed best with cross-validation mean F1 scores of 0.701, 0.596 and 0.546 on the same tasks. In both gene and pathway level classification, DNN convincingly outperformed support vector machine (SVM) model on every multiclass classification problem. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.
Recycling old drugs, rescuing shelved drugs and extendingpatents'lives make drug repositioning an attractive form of drug discovery. Drug repositioning accounts for approximately30% of the newlyUS Food and Drug Administration (FDA)-approved drugs and vaccines in recent years. The prevalence of drug-repositioning studies has resulted in a variety of innovative computational methods for theidentification of new opportunities for the use of old drugs.Questions often arise from customizingor optimizing these methods into efficient drug-repositioning pipelines for alternative applications. It requires a comprehensive understanding of the available methods gained by evaluatingboth biological and pharmaceutical knowledge and the elucidated mechanism-of-action of drugs.Here, we provide guidance for prioritizing and integrating drug-repositioning methods for specific drug-repositioning pipelines.
For more than half a century, the clinical development of anticancer drugs has followed a predictable and orderly set of sequential stages: phase 1 trials were designed to determine the drug’s safety, tolerability, and dose; phase 2 trials then explored the drug’s activity in a variety of cancers; and phase 3 trials compared the new drug with existing treatments and served as the basis for regulatory approval. Advances in our understanding of cancer biology in the past decade have led to both development of more effective drugs and improved patient selection made possible by early biomarker discovery and companion diagnostic . . .
The dramatic success of tyrosine kinase inhibitors (TKIs) has led to the widespread perception that chronic myeloid leukemia (CML) has become another chronic disease, where lifelong commitment to pharmacological control is the paradigm. Recent trials demonstrate that some CML patients who have achieved stable deep molecular response can safely cease their therapy without relapsing (treatment free remission; TFR). Furthermore, those who are unsuccessful in their cessation attempt can safely re-establish remission after restarting their TKI therapy. Based on the accumulated data on TFR we propose that it is now time to change our approach for the many CML patients who have achieved a stable deep molecular response on long-term TKI therapy. Perhaps half of these patients could successfully achieve TFR if offered the opportunity. For many of these patients ongoing therapy is impairing quality of life and imposing a heavy financial burden while arguably achieving nothing. This recommendation is based on the evident safety of cessation attempts and TFR in the clinical trial setting. We acknowledge that there are potential risks associated with cessation attempts in wider clinical practice, but this should not deter us. Instead we need to establish criteria for safe and appropriate TKI cessation. Clinical trials will enable us to define the best strategies to achieve TFR, but clinicians need guidance today about how to approach this issue with their patients. We outline circumstances in which it would be in the patient’s best interest to continue TKI, as well as criteria for a safe TFR attempt.
MOTIVATION: In the drug discovery field, new uses for old drugs, selective optimization of side activities and fragment-based drug design (FBDD) have proved to be successful alternatives to high-throughput screening. e-Drug3D is a database of 3D chemical structures of drugs that provides several collections of ready-to-screen SD files of drugs and commercial drug fragments. They are natural inputs in studies dedicated to drug repurposing and FBDD. AVAILABILITY: e-Drug3D collections are freely available at http://chemoinfo.ipmc.cnrs.fr/e-drug3d.html either for download or for direct in silico web-based screenings.
Much progress has been made over the past decade with the development of novel methods for addressing increasingly more complex multiplicity problems arising in confirmatory Phase III clinical trials. This includes traditional problems with a single source of multiplicity, for example, analysis of multiple endpoints or dose-placebo contrasts. In addition, more advanced problems with several sources of multiplicity have attracted attention in clinical drug development. These problems include two or more families of objectives such as multiple endpoints evaluated at multiple dose levels or in multiple patient populations. This paper provides a review of concepts that play a central role in defining and solving multiplicity problems (error rate definitions) and introduces main classes of multiple testing procedures widely used in clinical trials (nonparametric, semiparametric, and parametric procedures). The paper also presents recent advances in multiplicity research, including gatekeeping procedures for clinical trials with multiple sets of objectives. The concepts and methods introduced in the paper are illustrated using several case studies on the basis of real clinical trials. Software implementation of commonly used multiple testing and gatekeeping procedures is discussed. Copyright © 2012 John Wiley & Sons, Ltd.