Monthly Archives: March 2021

Pharmaceutical Predictivity

Pharmaceutical Attrition

Drug development is associated with a significant degree of attrition, which is generally thought to be in the 90 percent range, indicating that only about 10 percent of compounds entering development make it to regulatory submission, approval and market. During the 2000’s and 2010’s numerous papers were published on pharmaceutical attrition, attempting to arrive at an overall estimate of attrition, and the key reasons for attrition (e.g., Kola and Landis, 2004; Hay et al., 2014). Attrition rates have been reported to vary depending on different attributes, such as therapeutic areas, phase of development and pharmacologic targets; commonly cited causes of compound termination have included clinical safety, lack of efficacy, formulation, PK/bioavailability, commercial, toxicology, and cost of goods. 

A general comment concerning the published literature on attrition and its causes, as well as assessments within individual pharmaceutical companies, is that definitions and attributions have not been standardized, and common methodology has been lacking. It is also noted that even within a given company, when reviewing their compound termination databases, it’s not always clear what was considered the primary reason for the termination of a specific compound by the project team.

Considering the significant negative implications of candidate compound attrition for the pharmaceutical industry, principally in terms of R&D expenditure, opportunity cost and productivity, the question arises how the industry might be able to reduce attrition, and thus be able to bring more new therapies to market. As an example, R&D organizations might base how many candidate compounds to bring into development each year on the desired number of drug candidates to be submitted for regulatory submission at a future date following clinical development. Thus, assuming a goal of one regulatory submission per year, at least ten candidate compounds would need to be brought into development each year. In addition to the R&D expenditure, such an inefficient drug development process makes it harder to address the many unmet medical needs areas.

The purpose of this commentary is to present a framework to address attrition from a different perspective. It is not intended in any way to provide an extensive literature review of this complex topic; references are representative.

From Attrition to Predictivity

While different high-level categories have been proposed as causes of attrition, such as scientific reasons, technical reasons, commercial reasons and regulatory reasons, each with their specific causes, fundamentally the key causes of terminations are scientific, i.e., based on lack of efficacy, safety issues, and compound properties (compound properties are defined as determinants and descriptors of exposure). These are also the causes of terminations that together are by far the most common, and that lend themselves to addressing attrition by better understanding of the underlying scientific causes. This involves focusing on predictivity, i.e., how predictive are the preclinical methods and models of clinical performances:

PT = pe x ps x pc                                                                                                                 

Where PT stands for total predictivity with respect to scientific causes of terminations, and pe, ps and pc stand for the individual predictivities of efficacy, safety and compound properties, respectively. This is shown graphically below, and in an enlarged format here.

Note that the predictivities of these three scientific components are considered to be multiplicative, which assumes these are independent of each other. Also note that this leaves aside terminations by attrition categories such as cost of goods, formulation issues, budget/resource constraints, portfolio rationalization, potential values, patent issues and regulatory hurdles, as these are fundamentally unrelated to the three scientific causes mentioned above, and are typically business-related causes.

Predictivity Rates of Discovery and Preclinical Data

As was mentioned above, the data and methodologies that have been used to address attrition and attrition categories, as reflected in the published literature, have generally not been uniform or standardized. Yet in order to identify where there might be opportunities to reduce attrition, what’s needed are well-defined studies, with robust statistical analyses, of how predictive current preclinical studies of efficacy, safety and compound properties are with respect to clinical performance. With an overall success rate of only about 10%, for small molecules at least, one has to conclude that our understanding of how to confidently progress candidate compounds from preclinical to clinical is limited. Indeed, to quote Sir Peter Medawar, Nobel Laureate in Physiology and Medicine 1960: “No branch of science can be called truly mature until it has developed some form of predictive capability.”

A table of examples of preclinical methods and models of efficacy, safety and compound properties and examples of clinical failures due to efficacy failures, safety/toxicology failures or compound properties failures is shown here. At the present time, specific values, or ranges, for predictivities for efficacy, safety and compound properties are not available in the literature, but it is possible to arrive at a general range for these. While there are obviously a variety of complicating factors, such as the chemical properties of the candidate compounds, which therapeutic areas, and which specific parameters are being addressed for these scientific causes of termination, such guesstimates can be useful for identifying where the weaknesses are in the current preclinical-to-clinical paradigm lie. 

Predictivity of Compound Properties. Numerous papers have addressed predictivity of compound properties in man based on preclinical data using a variety of computational models to predict PK parameters in humans and compared these with the observed parameters in man, typically from early Phase I studies (e.g., PISC/PhRMA group, 2011; Jones et al., 2013). Parameters addressed have included PK parameters such as AUC, Cl, Vd, Cmax, t1/2 and F, and computational methods have included allometry, in vitro-in vivo extrapolation (IVIVE) and physiologically-based pharmacokinetics (PBPK). The most comprehensive assessment was from PISC/PhRMA group involved a cross-industry working group, which conducted an extensive analysis of a comprehensive and anonymized repository of preclinical and Phase I PK data for 108 compounds from a group of member companies. In five published papers, the group found that predicting events after oral administration within a factor of two was close to 50%. It should also be noted that in contrast to clinical safety and efficacy outcomes, a considerable deviation in compound properties (e.g., PK, bioavailability, food effect) from predicted values may not necessarily result in compound termination. 

Predictivity of Safety. There have also been numerous studies addressing predictivity of safety, typically based on the concordance of adverse effects in man with the findings from preclinical safety studies (e.g., Olson et al., 2000; Monticello et al., 2017). In general, these studies have found good concordance between the findings in preclinical and clinical studies, although there has typically been considerable variability with respect to different organ systems. While methodologies based on concordance are not specifically addressing predictivities, these are generally thought to be of a similar magnitude as those for compound properties, in the range of 0.5 to 0.7. It is noteworthy that regulatory requirements for preclinical studies for safety and compound properties are generally standardized and prescriptive. 

Predictivity of Efficacy. In contrast to predictivity of safety and compound properties, the situation with respect to how predictive preclinical efficacy studies and methods are with respect to efficacy in humans is significantly more limited. Numerous challenges in this area have been suggested, such as non-uniform preclinical studies and methodologies associated with preclinical animal models, small sample sizes and translational challenges. However, for illustrative purposes in this commentary, one can grossly estimate these to be in the 0.3 range, by solving for total predictivity of 0.10 and using 0.6 values for both predictivities of safety and compound properties. Of the three scientific causes of attrition, clearly predictivity of efficacy represents the weakest link in successful progression of candidate compounds from the preclinical to the clinical stage of development. 


These considerations suggest that in order to meaningfully reduce attrition rate, much more work is needed to better define the predictivities of preclinical methods and models, particularly those for efficacy. Work in this general area to date has clearly shown the value of precompetitive collaboration across the industry, as exemplified by recent work on compound properties (e.g., PISC/PhRMA group) and on safety (e.g., DruSafe group); both of these groups have illustrated the feasibility of precompetitive collaboration. It is important to note however that such collaborative work is both resource intensive and requires project management. Yet, it is hard to imagine how pharmaceutical productivity can be improved without it.

Predictivity => Productivity 


Hay M, Thomas DW, Craighead JL, et al. Clinical development success rates for investigational drugs. Nat. Biotechnol., 32: 40-51, 2014.

Jones HM, Mayawala K, Poulin P. Dose selection based on physiologically based pharmacokinetic (PBPK) approaches. The AAPS J. 15(2): 377-387, 2013

Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov., 3:711-716, 2004.

Monticello TM, Jones TW, Dambach DM, et al. Current nonclinical testing paradigm enables safe entry to First-in-Human clinical trials: The IQ consortium nonclinical to clinical translational database. Toxicol. Appl. Pharmacol., 334: 100-109, 2017.

Olsen H, Betton G, Robinson D, et al. Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul. Toxicol. Pharmacol., 32(1): 56-67, 2000.

PISC/PhRMA group. J. Pharmaceut. Sci, 100(10): 4045-4157, 2011. This issue has five papers from a PhRMA working group on predicting human pharmacokinetics, plus an editorial and a commentary by its two consultants.