Concept: Drug development
Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406 038 entries of clinical trial data for over 21 143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers.
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 over 4 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.
While timelines for clinical development have been extensively studied, there is little data on the broader path from initiation of research on novel drug targets, to approval of drugs based on this research. We examined timelines of translational science for 138 drugs and biologicals approved by the FDA from 2010-2014 using an analytical model of technology maturation. Research on targets for 102 products exhibited a characteristic (S-curve) maturation pattern with exponential growth between statistically defined technology initiation and established points. The median initiation was 1974, with a median of 25 years to the established point, 28 years to first clinical trials, and 36 years to FDA approval. No products were approved before the established point, and development timelines were significantly longer when the clinical trials began before this point (11.5 vs 8.5 years, p<0.0005). Technological maturation represents the longest stage of translation, and significantly impacts the efficiency of drug development.
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.