The EpiPen and Daraprim put pharmaceutical scientists on the defensive. What are they doing to address drug costs and long development timelines?
By Christopher R. McCurdy, Ph.D., FAAPS, President
As pharmaceutical scientists, we know the extensive research and exorbitant costs involved in taking a drug from the bench to the bedside. Today, it takes an average of 10 years and costs about $2.6 billion to get a drug from lab to market. Moreover, only about 5 percent of drug candidates ever make it to market.1 Today, public and government outcry over drug costs impels pharmaceutical scientists to find new ways to speed up the process and reduce costs, without compromising quality, safety, and efficacy.
The American Association of Pharmaceutical Scientists (AAPS) is excited to cover this issue in one of its end-to-end hot topics at the 2018 AAPS PharmSci 360 in November. Rapid and Cost-Effective Delivery of New Drugs to Patients will cover the high costs and lengthy timelines that continue to impede drug development, affecting patients, prescribers, and payers. Significant contributing factors include manufacturing issues, increasing regulatory expectations, and clinical failures resulting from insufficient efficacy or safety.
A review of news reports on this issue gives us hints on remedies that run the gamut from technology to regulation. Many focus on new technologies:
Artificial intelligence (AI) has received some press lately touting its ability to reduce the time between identifying a potential disease target to testing whether a drug candidate can hit that target. AI groups want to compress that time from four to six years to a single year and reduce the costs by 60 percent. In addition, they propose that AI algorithms can study drug candidate databases to reveal common features of chemical structures and to model how molecules can be put together in new ways and predict their behavior.1
The use of computer modeling and simulation is rapidly expanding. Two recent AAPS Newsmagazine articles demonstrated how in vivo pharmacokinetic data can accurately be simulated using either simple in silico inputs or more precise in vitro data. These simulations also provide an efficient means to test mechanistic hypotheses, e.g., lysosomal trapping, CYP mediated clearance, gastrointestinal physiology implications, or food effects. Such models provide critical input during the early stages of compound design and optimization.2 Furthermore, physiologically based pharmacokinetic (PBPK) modeling and simulation can support decisions on whether, when, and how to conduct certain clinical pharmacology studies and to support dosing recommendations for product labeling. It provides valuable information on clinical trial design and assists with clinical trial waivers. “Most important, PBPK helps answer a myriad of ‘what if’ questions that could not be answered without lengthy, expensive, logistically challenging, and sometimes ethically questionable clinical studies.”3
Big data has arrived, and managing that data has important repercussions in drug development. With new aggregation, storage, and analysis techniques, along with AI and machine learning, scientists can learn about trends and negative signals much earlier in clinical trials. Data managers claim this can shorten drug development, improve safety, and help create drugs at a faster pace.4
The cloud and increased digitalization has improved the efficiency of data collection and analysis. Health data from a variety of sources (e.g., labs, wearable devices, electronic diaries, and health records) and cloud computing helps scientists manage the data they receive. This real-world data is instrumental in targeting patient populations for clinical trials, cutting the time required for recruiting participants and cutting costs for this time-consuming and expensive leg of drug development.5
Other proposed ways to speed drugs to patients at a lower cost include identifying new indications for existing drugs (leveraging their known safety data and increasing phase 3 trial success rates), continuous manufacturing, and the Food and Drug Administration’s efforts to speed up generic drug approvals.
I expect all these innovations and more to be covered in Rapid and Cost-Effective Delivery of New Drugs to Patients at PharmSci 360. This end-to-end topic will feature 20 symposium presentations that deliver an update on factors leading to increased development and production burdens, as well as strategies for reducing costs and timelines across drug development. Ultimately, innovative approaches in these areas will speed up drug development and result in lower costs. Because this topic will touch on all five tracks of this new AAPS meeting (preclinical development, bioanalytics, clinical pharmacology, manufacturing and bioprocessing, formulation and quality), I look forward to following this hot topic across the pharmaceutical development spectrum.
I hope you plan to attend AAPS PharmSci 360. Check out this brand new meeting; I guarantee you’ll like what you see. Learn about programming and register.
REFERENCES
- Nelson B. Why Big Pharma and Biotech are betting big on AI. NBC News MACH. Published March 1, 2018. Accessed April 24, 2018.
- Chaudhuri SR, Bolger MB, Lawless M, Balakrishnan A, Morrison J. Physiologically Based Pharmacokinetic Modeling and Simulation for Drug Candidate Optimization and Selection. AAPS Newsmagazine. Published June 2016. Accessed April 24, 2018.
- Rostami-Hodjegan A. Darwich A, Leinfuss E. PBPK Modeling and Simulation: Yesterday’s Scientific Endeavor Is Today’s Regulatory Necessity. AAPS Newsmagazine. Published December 2017. Accessed April 24, 2018.
- Streeter J. Expanding data sources: faster drug development? Pharmaphorum. Published September 5, 2017. Accessed April 24, 2018.
- Wall M. How drug development is speeding up in the cloud. BBC News. Published February 21, 2017. Accessed April 24, 2018.