Pharmacokinetic–Pharmacodynamic Reasoning in Drug Discovery and Early Development
2009; Future Science Ltd; Volume: 1; Issue: 8 Linguagem: Inglês
10.4155/fmc.09.124
ISSN1756-8927
AutoresPiet H. van der Graaf, Johan Gabrielsson,
Tópico(s)Pharmaceutical studies and practices
ResumoFuture Medicinal ChemistryVol. 1, No. 8 EditorialFree AccessPharmacokinetic–pharmacodynamic reasoning in drug discovery and early developmentPiet H Van Der Graaf and Johan GabrielssonPiet H Van Der Graaf† Author for correspondencePharmacokinetics, Dynamics and Metabolism Department, Sandwich, Kent, UK. and Johan GabrielssonAstraZeneca R&D Mölnda, Discovery DMPK & BAC CVGI, S-431 83 Mölndal, Sweden. Published Online:13 Nov 2009https://doi.org/10.4155/fmc.09.124AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInRedditEmail Pharmacokinetics (PK) is the study of the time-course of the absorption, distribution, metabolism and excretion of drugs (i.e., what the body does to a drug). Pharmacodynamics (PD) is the study of the biological effects of drugs and their mechanisms of action (i.e., what a drug does to the body). By integrating PK and PD, it is possible to characterize the onset, intensity and duration of the pharmacological effects of a drug and to relate these to its mechanisms of action [1]. We believe that by better understanding the inter-relationships between PK and PD (integrative pharmacology or PKPD), we can shed light on situations where one or the other needs to be optimized in drug discovery and development. Hence, it is our contention that PKPD must play a greater and more central role in drug discovery and development if the full benefit of a drug's pharmacology is to be realized [2].In medicinal research programs, there is a risk of ascribing a crucial and central effect of the drug to a single property (e.g., plasma protein binding, intrinsic hepatic clearance and transcellular flux) or parameter (e.g., terminal plasma drug half-life and cerebrospinal fluid exposure). A typical example is a drug-discovery strategy that emphasizes the need for lower plasma protein binding than that of a comparative, in the belief that this will reduce the dose required. While such single variate structure–activity relationships (SARs) are relatively simple, they are rarely adequate and a multivariate approach is needed to simultaneously optimize pharmacokinetic and pharmacodynamic properties [3]. In a recent publication, we highlighted the need for optimizing a compound's characteristics on the combined value of its in vivo unbound plasma clearance and unbound potency, the reason being that these two parameters sometimes operate in different directions [2]. This can be carried out by studying the drug's behavior under transient conditions in intact in vivo systems and determining its pharmacological properties in the same experiments. Chemists can then relate these observations to the structural properties of the drug and translate them into further drug modifications. Many involved in medicinal research believe that maximum information about a pharmacological effect is obtained at the peak plasma drug concentration, in spite of frequently observed temporal differences between these two parameters. In fact, until recently, the observation that the magnitude of a pharmacological response lags behind that of the plasma concentration was regarded as the exception, being referred to as a 'PKPD mismatch'. However, thanks to the original pioneering work of Segre [4] and Sheiner [5] 'hysteresis' in concentration–effect relationships is now widely regarded as the norm in in vivo pharmacology. For this reason, concepts and methods to rationalize and interpret concentration–effect (or concentration–response) data are now routinely used by PKPD scientists [1]. In other words, it is the role of the PKPD scientist to bridge the gap between in vivo pharmacology and medicinal chemistry.Historically, PKPD has been a data-driven discipline with strong emphasis on (bio)statistical approaches such as nonlinear mixed effect (population PK) modeling [6]. The models employed tended to be rather empirical and goodness-of-fit was typically regarded as being more important than any lack of biological relevance. Although an empirical approach may work when the objective is to develop predictive models for interpolation within the same species, (e.g., predicting outcome with a dose that has yet to be tested), empirical models have significant limitations when it comes to extrapolating PK and PD properties between species. This is because the behavior of drugs may change dramatically between different systems, be they normal or pathological. Indeed, it is increasingly being recognized that, even across in vitro assays, compounds may display 'pluridimensional efficacy' [7], which challenges the traditional way pharmacologists and medicinal chemists classify and characterize drugs. Not surprisingly, with increased interest in its relevance to preclinical research, PKPD has evolved towards a more mechanistic approach and (semi)mechanistic PKPD models are now advocated not only by academic and industrial researchers, but also regulators [8–10]. Recently, this has led to the emergence of systems pharmacology. Broadly speaking, this is the quantitative analysis of the dynamic interactions between drug(s) and a biological system. In other words, systems pharmacology aims to understand the behavior of the system as a whole, as opposed to the behavior of its individual constituents; thus, it has become the interface between PKPD and systems biology. It applies the concepts of systems engineering, systems biology and PKPD to the study of complex biological systems through iteration between computational and/or mathematical modeling and experimentation (based on a report from the Academy of Medical Sciences and The Royal Academy of Engineering [101]). Examples of the growing interest in this field are the recent workshops Integrating Systems Approaches into Pharmaceutical Sciences[11] and Quantitative and Systems Pharmacology[102] organized by The European Federation for Pharmaceutical Sciences and the National Institutes of Health, respectively. These aimed to address the question of the ways in which systems biology, modeling and more quantitative techniques could be applied to pharmacology and drug discovery/action, now and in the foreseeable future.Although PKPD modeling and simulation approaches have formed part of clinical R&D since the 1980s, 'model-based drug-development' paradigms are a far more recent phenomenon, being increasingly advocated by both regulatory agencies and pharmaceutical research organizations as a way of improving efficiency and productivity in pharmaceutical R&D [10,12]. Indeed, many companies and the US FDA have formed groups dedicated to the discipline of 'pharmacometrics' [13]. This is the science of developing and applying mathematical and statistical methods to characterize, understand and predict a drug's behavior in terms of its PK, PD and biomarker outcomes [14]. Recently, it has also been suggested that PKPD modeling and simulation can play a significant role in early preclinical drug discovery and can provide a framework for 'translational' research that links, in a quantitative manner, the interactions between a drug (or combination of drugs), pharmacological targets, physiological pathways and, ultimately, integrated disease systems [8]. Another important application of preclinical PKPD is that a quantitative understanding of in vivo pharmacological exposure–response relationship can aid the identification and selection of suitable biomarkers in early exploratory clinical development. Until recently, translational PKPD, a relatively new area in drug discovery, was mainly restricted to academic research [9,15,16]. However, it is increasingly being recognized that successful implementation of PKPD reasoning in early drug discovery could have at least as much impact on the overall efficiency and success of pharmaceutical research as comparable investments in late-stage modeling and simulation [2,17]. This is because, arguably, the most significant challenge facing the pharmaceutical industry is compound attrition, resulting from the failure of preclinical efficacy and safety model data to translate into human proof-of-mechanism/concept studies [18].It is, therefore, imperative that there be an integrative and quantitative approach among discovery and development scientists, including medicinal chemists. This will require greater openness in academic institutions, enabling more effective integration of in vivo experimentation methods with quantitative approaches, such as receptor theory and PKPD modeling. Professionally, we often find there is a lack of any real, quantitative understanding of fundamental PKPD concepts such as the determinants of target engagement. In an effort to communicate the advantages of an integrative, quantitative approach to pharmacology to workers in departments of metabolism and pharmacokinetics (DMPK), biology, chemistry and the safety sciences, various companies have invested significantly in in-house educational programs for scientists and managers at several levels. These courses cover aspects of PKPD from the basic to the advanced. Externally, there are also growing efforts to provide educational programs to increase the awareness of and expertise in PKPD among drug-discovery scientists. For example, there are educational programs sponsored by the Swedish, British and American Pharmaceutical Societies [103,104] and the British Pharmacological Society [105] in postgraduate courses at several universities. Perhaps of particular interest to medicinal chemists are the short PKPD courses organized by the American Chemical Society [106] and the Swedish Pharmaceutical Society [107].We believe there are also significant opportunities for modeling and simulation in the areas of safety pharmacology and toxicology. For example, as PKPD methodology is adaptive, it provides better tools to assess and predict safety margins than conventional methods. Hence, it is increasingly used as a key component in drug-discovery strategies to reduce cardiovascular risk factors. Likewise, PKPD analysis of the time course of an effect may provide insights into compound behavior that are not revealed with conventional (steady-state) experiments. For example, as mentioned previously, it is not uncommon for there to be a marked delay between the time courses of a drug's concentration and its effect (known as 'hysteresis') and, in some cases, this has led to a redefinition of therapeutic index. Finally, experimental designs based on PKPD principles are also more efficient, allowing rapid assessment of safety at very early stages of drug discovery [2,19].So where does this leave us? Today, the process of drug discovery is still too fragmented, with different disciplines delivering independent data to medicinal chemists according to their own screening scheme. Furthermore, there is often little in the way of real integration of pharmacology with DMPK. For more efficient and effective kinetic–dynamic reasoning, the drug-discovery culture must change so that interdisciplinary communication increases and the emphasis changes from a paradigm of 'data gathering' to one of 'knowledge generation'. In order to address fundamental gaps in skills and in the understanding of quantitative PKPD methodology among pharmacologists, DMPK scientists and medicinal chemists, there is a need for investment in internal and external interdisciplinary training programs. Ideally, these should be based on real-life case studies, as these can be used to illustrate the value of early PKPD in drug discovery not only through success stories but also by highlighting the negative impact of a lack of kinetic–dynamic reasoning.It is evident that we consider PKPD to be integrative, quantitative and adaptive in in vivo pharmacology. In view of this, it is easy to see why it fails to automatically position itself as a subdiscipline of either DMPK or biology departments, as pharmacology is not the natural territory of DMPK and quantitative modeling is not the norm in most biology departments. As shown by the original pioneers of our field, such as Jacques van Rossum [20] and Gerhard Levy [21], the key to success lies in integration. Consequently, PKPD will only reach its full potential in drug discovery when there is effective and efficient interdisciplinary cross-fertilization. With this in mind, it is no coincidence that one of us originally trained as an in vivo pharmacokineticist and the other as an in vitro receptor pharmacologist.Financial & competing interests disclosurePiet Van Der Graaf is an employee of Pfizer (UK). Johan Gabrielsson is an employee of AstraZeneca (Sweden). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.No writing assistance was utilized in the production of this manuscript.Bibliography1 Gabrielsson J, Weiner D. Pharmacokinetic & Pharmacodynamic Data Analysis – Concepts and Applications (Fourth Edition). 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Johan Gabrielsson is an employee of AstraZeneca (Sweden). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.No writing assistance was utilized in the production of this manuscript.PDF download
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