American Journal of Pharmacology and Toxicology

Impact of Quantitative Pharmacology on Drug Development

Ana Ruiz-Garcia and Kourosh Parivar

DOI : 10.3844/ajptsp.2014.203.205

American Journal of Pharmacology and Toxicology

Volume 9, Issue 4

Pages 203-205

Editorial

Thesuccess of drug development rests over two well defined pillars, a goodunderstanding of the drug’s clinical pharmacology and the appropriate targetpopulation for whom the clinical use is intended. The label for human prescription drugsrequires a good understanding of the pharmacological effects (pharmacodynamicsor PD) and the mechanism of action of the drug as well as detailed informationof the drug’s Pharmacokinetics (PK): Absorption, distribution, metabolism andexcretion. Theunderstanding of the PK characteristics and PD effects (desired or adverse)will provide educated recommendations about the effective dose, dosing regimen,potential drug-drug interactions and hence contraindications and warnings.  Further, the influence of demographic factorson the PK and PD attributes (e.g., age, sex, race, hepatic or renal impairment)need to be well understood to provide the appropriate guidance to patients andcaregivers for patients in specific populations (pediatric, geriatric, organimpairment, pregnancy, etc). In summary, a very extensive portion of the labelcovers clinical pharmacology topics, the following label sections are mainlysupported by clinical pharmacology knowledge collected throughout the course ofdrug development:

  • Dosage and administration: Including the dose, frequency of administration and route of administration
  • Clinical Pharmacology:
    • Mechanism of Action
    • Pharmacodynamics
    • Pharmacokinetics (ADME)
    • Pharmacogenomics (if appropriate)
  • Drug interactions
  • Contraindications
  • Use in specific populations

The methodology used to support the label claims in the last decade has beenstrongly influenced from the industry shift towards “learning Vs. confirming”introduced. The traditional way of drug development has been much focused onempirical testing of New Chemical Entities (NCE) through classical Phase I, IIand III drug development with subsequent high failure rate in Phase III.However, with the learning Vs confirming paradigm the intellectual focus ofclinical drug development has been on understanding the PK and PK/PD characteristicsof the NCEs during each stage of drug development and using that knowledge inplanning the next step. Planning and analyzing has become as important,if not more, than execution. Quantitative pharmacology has clearly emerged askey component of drug development and decision making.  Very intuitive andstraight forward definitions for 2 of the big aspects of clinical pharmacology,PK and PD were provided, which later (1984) Leslie Benet introduced in English:

Pharmacokineticsis what the body does to the drug-pharmacodynamics is what the drug does to thebody.

Indicated quantitative Model-Based Drug Development (MBDD) as a fundamental tool to improve efficiency and success rate in drug development.  Regulatory agencies including the US Food and Drug Administration (FDA) and European Agency for the Evaluation of Medicinal Products (EMA) have acknowledged and reflected the use of MBDD in a variety of guidances for industry briefly discussed in this editorial: First in Human (FIH) dose selection, exposure-response relationships, QT/QTc, drug interaction studies, renal and hepatic impairment studies, End-of-Phase 2A (EOP2A) meeting, population pharmacokinetics and adaptive design trials.

Both FDA and EMA havestated the importance of MBDD to justify the selection of FIH dose in theirguidance indicating that the No Observed Adverse Effect Level (NOAEL)determined in preclinical toxicology studies in the most sensitive and relevantanimal species should not be the only approach. Quoting the guidance document:

Although the process outlined in this document uses observedtoxicities, administered doses and an algorithmic approach to calculate the MaximumRecommended Starting Dose (MRSD), an alternative approach could be proposedthat places primary emphasis on animal pharmacokinetics and modeling ratherthan dose”.

 The US FDA guidance of exposure-responserelationships emphasizes the importance of MBDD in exposure-response studiesand suggests that model-based analysis and simulation be applied to analyzeexposure-response data from clinical as well as preclinical studies. PK/PDsimulation is also suggested as a way of predicting expected relationshipsbetween exposure and response in situations where real data is sparse orabsent.

Evaluation of drug effects on thestandard Electrocardiogram (ECG) intervals and waveforms is considered afundamental component of the safety database of any new drug application.  Analyses of central tendency, categoricalanalyses of the QT/QTc interval as well as the relationship between drug exposureand QT/QTc interval changes are outlined in the FDA guidance. PK/PD analysis ofthe relationship between exposure, often represented as Cmax and QTprolongation may provide additional information to assist in the planning andinterpretation of studies assessing cardiac repolarization.

Quantitativepharmacology can be valuable in characterizing the clinical impact of known ornewly identified interactions and making recommendations for dosageadjustments. Results from population pharmacokinetic analyses could beinformative and sometimes conclusive when the clinical studies are adequatelydesigned to detect significant changes in drug exposure due to Drug-DrugInteractions (DDIs).  Moreover, it mayalso help to detect unsuspected DDIs and provide further evidence of theabsence of a DDI when this is supported by prior outcome and mechanistic data.

A study in patients with impaired renalor hepatic function to characterize the PK behavior of the study drug isrecommended when either renal or hepatic impairment is likely to significantlyalter the PK of the drug and/or its active metabolites. Both Guidance’s, agreed on theimportance of modeling renal and hepatic function and PK parameters with thegoal of providing quantitative basis for dosage recommendations.

FDA associatessummarized the experience across 11 pilot EOP2A meetings between 2004 and 2006before the guidance was issued. These meetings focused on discussing theexposure-response information during early drug development with the objectivesof improving the efficiency of Phase 2B and Phase 3 drug development.  Models were built based on Phase 1 and Phase2A data such as dose response, disease conditions, placebo effect and baselinedata. The experience of these pilot meetings suggested that if MBDD strategy isembraced in early clinical development, late-stage drug development wouldbecome more efficient and there would be fewer disappointments prior to andafter drug approval.

Both,EMA and FDA have drafted guidance’s on how to present the results of apopulation PK analysis, including guidance on the content of the analysis planfor the population analysis. The agencies recommend the use of populationanalysis in drug development, model validation methods and the appropriatedocumentation to provide in the population report intended for submission.

AllMBDD carries a strong statistical component, in fact, in drug development theuse of adequate statistical methods is key in designing, conducting, evaluatingand interpreting results. Adaptive(flexible) designs are promoted by EMA and FDA. Model based designs use accumulating data to decide on how to modifyaspects of the study as it continues, without undermining the validity andintegrity of the trial. The main goal is to learn from the accumulating dataand to apply what is learned as quickly as possible.

In summary, quantitativepharmacology has been identified by regulatory agencies as a valuablediscipline with a profound impact in the efficiency and success rate of drug developmentprograms. Learning and confirming using a mathematical approach with theappropriate statistical rigor enables critical decision making bringing wellunderstood and characterized medicines to patients.

Copyright

© 2014 Ana Ruiz-Garcia and Kourosh Parivar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.