- Email: [email protected]
Lead PK Commentary: Predicting Human Pharmacokinetics
CLINICAL TRIALS AND TRANSLATIONAL MEDICINE COMMENTARIES Lead PK Commentary: Predicting Human Pharmacokinetics Malcolm Rowland,1 Leslie Z Benet2 1
School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester M13 9PT, UK
2
Department of Bioengineering and Therapeutic Sciences, School of Pharmacy, University of California San Francisco, San Francisco, California 94143 Received 6 May 2011; accepted 6 May 2011 Published online 31 May 2011 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.22637 Keywords: 2-dimensional gel electrophoresis; 5-Aminolevulinic Acid; Ab initio calculations; ABC transporters; Absorption
Selecting the best compounds, often from among many, to bring forward for evaluation in humans is a difficult and challenging task. Recognizing this problem, the Pharmaceutical Research Manufacturers Association (PhRMA) established a Pharmaceutical Innovations Steering Committee (PISC) with the aim of improving the chances of success. In addition to the two task forces focusing on safety and efficacy, another focused on prediction of human pharmacokinetics (PK). Published in this issue of the Journal, in the form of five papers, is the outcome of the research undertaken by this last group. Although there have been substantial improvements in our understanding of factors controlling the PK of a compound, such that it is often now not the primary reason for failure during clinical drug development, there is still a great need to improve our prediction methods for several reasons. One is the need to reduce the waste of resources (material, time, and cost) spent unnecessarily on poor compounds that could have otherwise been better spent. Another is the ethical one of exposing animals to distress and trauma with poor compounds that will be dropped subsequently during development. A third is that better prediction of human PK will not only aid in the better estimate of the doses needed to ensure adequate systemic exposure of active principle in first-in-human (FIH) studies, but also subsequently facilitate the improved ease of development and optimal therapeutic use of compounds. There are several unique features of this study by PhRMA. It is the first wherein PhRMA member comCorrespondence to: Malcom Rowland (E-mail: mrow190539@ aol.com) Journal of Pharmaceutical Sciences, Vol. 100, 4047–4049 (2011) © 2011 Wiley-Liss, Inc. and the American Pharmacists Association
panies collectively provided nonclinical and FIH PK data on low molecular weight lead clinical candidate compounds (108 following oral administration in humans, of which 19 had also been given intravenously), exhibiting a wide array of physicochemical and structural properties. The data were then anonymized by PhRMA prior to data analysis. Furthermore, the human data were initially withheld from the data analyst, and only made available after all the predictions of human PK had been made, thereby mimicking the prospective situation faced by drug developers, while minimizing the likelihood of bias. This approach contrasts with that employed in the vast majority of previous studies in which the investigator knew the human data and tested, and sometimes “adjusted,” the prediction methodology retrospectively. Furthermore, there are a multitude of published methodologies, each often claiming to be superior to others, so a critical part of the current project was to comprehensively evaluate all published methodologies on the same data set, an onerous task. Finally, the compounds that comprised the current study are those under recent development (as only these have all the necessary physicochemical, in vitro, as well as in vivo data), in contrast to many published reports that evaluated decades old compounds. This is confirmed by the distribution of PhRMA compounds within the Biopharmaceutics Classification System, with 15%, 57%, 12%, and 23% in class 1, 2, 3, and 4, respectively, compared with 39%, 31%, 23%, and 7% for marketed products cited by the authors. In keeping with this distribution, the majority of PhRMA compounds were primarily eliminated by metabolism. Accordingly, the current findings are likely to be more relevant to those engaged in drug development today.
JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 10, OCTOBER 2011
4047
4048
ROWLAND AND BENET
Broadly, the prediction methodologies have been divided into three areas: allometry, in vitro–in vivo extrapolation (IVIVE), and physiologically based pharmacokinetics (PBPK). Allometry, involving scaling to humans PK data in test animals (often rat and dog, and in this study also frequently in mouse and nonhuman primate), based on body size, has a long history and is the mainstay approach by industry. IVIVE, which has been applied to predict a parameter, such as hepatic metabolic clearance, based on human-derived tissues or expression systems, is more recent and aims to address particularly the known species differences in active processes. The latest newcomer to see increasing application is PBPK, which incorporates a combination of physicochemical and in vitro biological data of a compound into a whole body physiologically based model, comprising independent data on tissue size, composition, blood flow, and physiologic function, to predict the temporal pattern of a compound in body fluids and tissues. Animal data are not inherently required in PBPK prediction of human PK, although development and verification of the methodology are often undertaken in animals. IVIVE may be regarded as a component of PBPK. The findings of the project are illuminating. They show that although some prediction methods are definitely better than others, there is currently no universally outstanding method. Also, as expected, the prediction of human disposition kinetics following intravenous administration is much better than the prediction of events following oral administration. Indeed, even the best methods could only predict events after oral administration, within a factor of two, in the order of 45% of the time, with a tendency to under predict the area under the curve, probably primarily due to an underprediction of oral bioavailability. There are likely to be many reasons for this failure, including the complex physiology of the gastrointestinal tract coupled with the complex processes occurring during absorption, especially following administration of sparingly soluble compounds, with markedly different formulations often used in preclinical development than tested in humans. Thus, it would be expected that predictions should be better for class 1, highly soluble–highly permeable compounds, as was found and reported in paper 5 for bioavailability predictions. Although popular, interanimal scaling allometrically is essentially empirical, which probably explains the myriad of modifications proposed by various investigators over the years, and evaluated in the current project. The distinction is also brought out clearly between prediction of a parameter value, such as clearance or volume of distribution and, ultimately more important, prediction of a concentration–time profile, given that concentration over time drives response, even though originally attention was given JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 10, OCTOBER 2011
to prediction of temporal events using the Dedrick plot and its modifications. Perhaps one reason for the more common evaluation of a parameter rather than a temporal profile is the lack of a universally acceptable method to evaluate similarity of shape. The authors of the project have addressed this problem in a novel way based on a series of reasonable criteria, which, although informative and pragmatic, may be questioned by those interested in statistical rigor, although some of us recall the many debates of a similar nature in bioequivalence testing, without clear resolution. That said, attempts to predict the concentration–time profile allometrically were generally poor. Interestingly, when predicting human concentration–time profiles, no one preclinical species was found to be superior to the others nor were multiple species data shown to be superior to data from one species. Biologics aside, this finding raises the question as to the value of using non-human primates for such purposes. The results here also suggest that rodent data, usually available at an early stage of the process, for example, discovery, could be equally (but probably not sufficiently) as reliable as the data obtained in dog and monkey in the late candidate selection stage. However, considering the poor predictability of bioavailability, one must recognize that, especially for the many poorly soluble drug candidates, the later stage candidate selection studies in large animals almost always involve formulations that are closer to that used in FIH studies than used in the discovery stage rodent studies. Physiologically based pharmacokinetics was evaluated using a generic model. Perhaps surprisingly to some, on average, this methodology fared no better than allometry in predicting human PK, and sometimes proved inferior. However, this needs to be put in perspective. Unlike allometry, which fundamentally can progress no further, PBPK, which is still in its infancy, is highly mechanistic, and current failures help highlight a lack of understanding of particular processes. Other advantages of this bottom-up approach are that it explains the observed in vivo behavior of drugs, requires minimal resources, including being animal sparing, and can be employed upstream, by linking processes to physicochemical and structure properties, to help guide the drug discovery teams in the better design of compounds. In addition, unlike allometry (with the possible exception of its use in pediatrics), PBPK models extend beyond dose selection in FIH studies, by helping to guide various aspects of clinical development, such as prediction of the impact of drug–drug interactions, age, disease, and so forth on PK, design of experiments, as well as subsequent therapeutic use of compounds. Although the PhRMA project will hopefully help in evidence-based choice of prediction methodologies, it has its limitations, which need to be kept in mind. DOI 10.1002/jps
LEAD PK COMMENTARY
First, although the ranges of structures and physicochemical properties are quite wide, these undoubtedly do not cover all chemical space encountered in drug development, so that companies concentrating in particular areas of chemical space will need to place the current findings into perspective. Although many companies provided all the expected in vitro physiochemical and biological data, this was by no means universal, which reflects the state of acceptance of such data in decision-making. In the area of PBPK, a generic model was employed, which while very helpful in gaining some insights does not incorporate all the many features found in commercial PBPK software, which if employed may affect the success rate of this methodology. In addition, in all cases evaluated, allometry, IVIVE, and PBPK, analysis and prediction dealt with mean data, whereas information regarding variability can be equally important when considering FIH studies, and beyond. So where are things likely to go from here? Despite its limitations, allometry is likely to remain the maintained approach by industry for prediction of human PK in candidate selection for some years yet, although the benefit of using non-human primates is questionable. However, as our understanding of processes controlling PK improves, and the in vitro human biologic systems become increasingly more predictive of in vivo events, we will see the increasing adoption of the more mechanistic PBPK as the firstline approach. Pharmaceutical scientists are encouraged to collaborate to progress these developments. Hopefully, the path taken by PhRMA, to act as an anonymized repository of data and as a facilitator of such collaboration, will be the forerunner of future developments in other areas of the pharmaceutical sciences.
DOI 10.1002/jps
4049
Today, much is written and discussed in workshops and consensus panels about the barriers in the drug approval process. Concerns are expressed about the money available, the discovery and development process itself, and the adequacy of the regulatory agencies. There is a great interest in developing mechanisms for cooperative methodologies at the precompetitive stage. We highly commend PhRMA and the scientists from the 12 companies, and the data analysts, who carried out the present initiative and believe that it serves as a model for other future approaches. A barrier question was identified, that is, how useful are animal data in predicting human PK? Real data in multiple animal species for a large number of drugs were assembled, anonymized, and evaluated in terms of the many methodologies proposed for using such data to predict human results. Then, these analyses were compared with the observed human PK findings. There were also analyses of the adequacy or lack thereof for in silico and in vitro predictive parameters. Thus, PhRMA and the regulatory agencies now have very useful information about the adequacy/ inadequacy of animal PK studies. But, in our opinion, of even greater credit to PhRMA and the 12 companies is their willingness to make this anonymized data set freely available. Here is the real potential for advancing drug discovery and development science. Making the anonymized data generally available required a significant additional effort from the PhRMA scientists. When the barriers to improve the drug discovery and development process are discussed, the hurdles added by corporate legal policies need to be included together with money, scientific approaches, and regulatory requirements. In this case, with much effort, a very valuable set of data has become available to the pharmaceutical sciences community.
JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 10, OCTOBER 2011
School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester M13 9PT, UK
2
Department of Bioengineering and Therapeutic Sciences, School of Pharmacy, University of California San Francisco, San Francisco, California 94143 Received 6 May 2011; accepted 6 May 2011 Published online 31 May 2011 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.22637 Keywords: 2-dimensional gel electrophoresis; 5-Aminolevulinic Acid; Ab initio calculations; ABC transporters; Absorption
Selecting the best compounds, often from among many, to bring forward for evaluation in humans is a difficult and challenging task. Recognizing this problem, the Pharmaceutical Research Manufacturers Association (PhRMA) established a Pharmaceutical Innovations Steering Committee (PISC) with the aim of improving the chances of success. In addition to the two task forces focusing on safety and efficacy, another focused on prediction of human pharmacokinetics (PK). Published in this issue of the Journal, in the form of five papers, is the outcome of the research undertaken by this last group. Although there have been substantial improvements in our understanding of factors controlling the PK of a compound, such that it is often now not the primary reason for failure during clinical drug development, there is still a great need to improve our prediction methods for several reasons. One is the need to reduce the waste of resources (material, time, and cost) spent unnecessarily on poor compounds that could have otherwise been better spent. Another is the ethical one of exposing animals to distress and trauma with poor compounds that will be dropped subsequently during development. A third is that better prediction of human PK will not only aid in the better estimate of the doses needed to ensure adequate systemic exposure of active principle in first-in-human (FIH) studies, but also subsequently facilitate the improved ease of development and optimal therapeutic use of compounds. There are several unique features of this study by PhRMA. It is the first wherein PhRMA member comCorrespondence to: Malcom Rowland (E-mail: mrow190539@ aol.com) Journal of Pharmaceutical Sciences, Vol. 100, 4047–4049 (2011) © 2011 Wiley-Liss, Inc. and the American Pharmacists Association
panies collectively provided nonclinical and FIH PK data on low molecular weight lead clinical candidate compounds (108 following oral administration in humans, of which 19 had also been given intravenously), exhibiting a wide array of physicochemical and structural properties. The data were then anonymized by PhRMA prior to data analysis. Furthermore, the human data were initially withheld from the data analyst, and only made available after all the predictions of human PK had been made, thereby mimicking the prospective situation faced by drug developers, while minimizing the likelihood of bias. This approach contrasts with that employed in the vast majority of previous studies in which the investigator knew the human data and tested, and sometimes “adjusted,” the prediction methodology retrospectively. Furthermore, there are a multitude of published methodologies, each often claiming to be superior to others, so a critical part of the current project was to comprehensively evaluate all published methodologies on the same data set, an onerous task. Finally, the compounds that comprised the current study are those under recent development (as only these have all the necessary physicochemical, in vitro, as well as in vivo data), in contrast to many published reports that evaluated decades old compounds. This is confirmed by the distribution of PhRMA compounds within the Biopharmaceutics Classification System, with 15%, 57%, 12%, and 23% in class 1, 2, 3, and 4, respectively, compared with 39%, 31%, 23%, and 7% for marketed products cited by the authors. In keeping with this distribution, the majority of PhRMA compounds were primarily eliminated by metabolism. Accordingly, the current findings are likely to be more relevant to those engaged in drug development today.
JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 10, OCTOBER 2011
4047
4048
ROWLAND AND BENET
Broadly, the prediction methodologies have been divided into three areas: allometry, in vitro–in vivo extrapolation (IVIVE), and physiologically based pharmacokinetics (PBPK). Allometry, involving scaling to humans PK data in test animals (often rat and dog, and in this study also frequently in mouse and nonhuman primate), based on body size, has a long history and is the mainstay approach by industry. IVIVE, which has been applied to predict a parameter, such as hepatic metabolic clearance, based on human-derived tissues or expression systems, is more recent and aims to address particularly the known species differences in active processes. The latest newcomer to see increasing application is PBPK, which incorporates a combination of physicochemical and in vitro biological data of a compound into a whole body physiologically based model, comprising independent data on tissue size, composition, blood flow, and physiologic function, to predict the temporal pattern of a compound in body fluids and tissues. Animal data are not inherently required in PBPK prediction of human PK, although development and verification of the methodology are often undertaken in animals. IVIVE may be regarded as a component of PBPK. The findings of the project are illuminating. They show that although some prediction methods are definitely better than others, there is currently no universally outstanding method. Also, as expected, the prediction of human disposition kinetics following intravenous administration is much better than the prediction of events following oral administration. Indeed, even the best methods could only predict events after oral administration, within a factor of two, in the order of 45% of the time, with a tendency to under predict the area under the curve, probably primarily due to an underprediction of oral bioavailability. There are likely to be many reasons for this failure, including the complex physiology of the gastrointestinal tract coupled with the complex processes occurring during absorption, especially following administration of sparingly soluble compounds, with markedly different formulations often used in preclinical development than tested in humans. Thus, it would be expected that predictions should be better for class 1, highly soluble–highly permeable compounds, as was found and reported in paper 5 for bioavailability predictions. Although popular, interanimal scaling allometrically is essentially empirical, which probably explains the myriad of modifications proposed by various investigators over the years, and evaluated in the current project. The distinction is also brought out clearly between prediction of a parameter value, such as clearance or volume of distribution and, ultimately more important, prediction of a concentration–time profile, given that concentration over time drives response, even though originally attention was given JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 10, OCTOBER 2011
to prediction of temporal events using the Dedrick plot and its modifications. Perhaps one reason for the more common evaluation of a parameter rather than a temporal profile is the lack of a universally acceptable method to evaluate similarity of shape. The authors of the project have addressed this problem in a novel way based on a series of reasonable criteria, which, although informative and pragmatic, may be questioned by those interested in statistical rigor, although some of us recall the many debates of a similar nature in bioequivalence testing, without clear resolution. That said, attempts to predict the concentration–time profile allometrically were generally poor. Interestingly, when predicting human concentration–time profiles, no one preclinical species was found to be superior to the others nor were multiple species data shown to be superior to data from one species. Biologics aside, this finding raises the question as to the value of using non-human primates for such purposes. The results here also suggest that rodent data, usually available at an early stage of the process, for example, discovery, could be equally (but probably not sufficiently) as reliable as the data obtained in dog and monkey in the late candidate selection stage. However, considering the poor predictability of bioavailability, one must recognize that, especially for the many poorly soluble drug candidates, the later stage candidate selection studies in large animals almost always involve formulations that are closer to that used in FIH studies than used in the discovery stage rodent studies. Physiologically based pharmacokinetics was evaluated using a generic model. Perhaps surprisingly to some, on average, this methodology fared no better than allometry in predicting human PK, and sometimes proved inferior. However, this needs to be put in perspective. Unlike allometry, which fundamentally can progress no further, PBPK, which is still in its infancy, is highly mechanistic, and current failures help highlight a lack of understanding of particular processes. Other advantages of this bottom-up approach are that it explains the observed in vivo behavior of drugs, requires minimal resources, including being animal sparing, and can be employed upstream, by linking processes to physicochemical and structure properties, to help guide the drug discovery teams in the better design of compounds. In addition, unlike allometry (with the possible exception of its use in pediatrics), PBPK models extend beyond dose selection in FIH studies, by helping to guide various aspects of clinical development, such as prediction of the impact of drug–drug interactions, age, disease, and so forth on PK, design of experiments, as well as subsequent therapeutic use of compounds. Although the PhRMA project will hopefully help in evidence-based choice of prediction methodologies, it has its limitations, which need to be kept in mind. DOI 10.1002/jps
LEAD PK COMMENTARY
First, although the ranges of structures and physicochemical properties are quite wide, these undoubtedly do not cover all chemical space encountered in drug development, so that companies concentrating in particular areas of chemical space will need to place the current findings into perspective. Although many companies provided all the expected in vitro physiochemical and biological data, this was by no means universal, which reflects the state of acceptance of such data in decision-making. In the area of PBPK, a generic model was employed, which while very helpful in gaining some insights does not incorporate all the many features found in commercial PBPK software, which if employed may affect the success rate of this methodology. In addition, in all cases evaluated, allometry, IVIVE, and PBPK, analysis and prediction dealt with mean data, whereas information regarding variability can be equally important when considering FIH studies, and beyond. So where are things likely to go from here? Despite its limitations, allometry is likely to remain the maintained approach by industry for prediction of human PK in candidate selection for some years yet, although the benefit of using non-human primates is questionable. However, as our understanding of processes controlling PK improves, and the in vitro human biologic systems become increasingly more predictive of in vivo events, we will see the increasing adoption of the more mechanistic PBPK as the firstline approach. Pharmaceutical scientists are encouraged to collaborate to progress these developments. Hopefully, the path taken by PhRMA, to act as an anonymized repository of data and as a facilitator of such collaboration, will be the forerunner of future developments in other areas of the pharmaceutical sciences.
DOI 10.1002/jps
4049
Today, much is written and discussed in workshops and consensus panels about the barriers in the drug approval process. Concerns are expressed about the money available, the discovery and development process itself, and the adequacy of the regulatory agencies. There is a great interest in developing mechanisms for cooperative methodologies at the precompetitive stage. We highly commend PhRMA and the scientists from the 12 companies, and the data analysts, who carried out the present initiative and believe that it serves as a model for other future approaches. A barrier question was identified, that is, how useful are animal data in predicting human PK? Real data in multiple animal species for a large number of drugs were assembled, anonymized, and evaluated in terms of the many methodologies proposed for using such data to predict human results. Then, these analyses were compared with the observed human PK findings. There were also analyses of the adequacy or lack thereof for in silico and in vitro predictive parameters. Thus, PhRMA and the regulatory agencies now have very useful information about the adequacy/ inadequacy of animal PK studies. But, in our opinion, of even greater credit to PhRMA and the 12 companies is their willingness to make this anonymized data set freely available. Here is the real potential for advancing drug discovery and development science. Making the anonymized data generally available required a significant additional effort from the PhRMA scientists. When the barriers to improve the drug discovery and development process are discussed, the hurdles added by corporate legal policies need to be included together with money, scientific approaches, and regulatory requirements. In this case, with much effort, a very valuable set of data has become available to the pharmaceutical sciences community.
JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 10, OCTOBER 2011