PK/PD modelling and beyond: impact on drug development

Current progress and limitations of AAV mediated delivery of protein therapeutic genes and the importance of developing quantitative pharmacokinetic/pharmacodynamic (PK/PD) models

While protein therapeutics are one of the most successful class of drug molecules, they are expensive and not suited for treating chronic disorders that require long-term dosing. Adeno-associated virus (AAV) mediated in vivo gene therapy represents a viable alternative, which can deliver the genes of protein therapeutics to produce long-term expression of proteins in target tissues. Ongoing clinical trials and recent regulatory approvals demonstrate great interest in these therapeutics, however, there is a lack of understanding regarding their cellular disposition, whole-body disposition, dose-exposure relationship, exposure-response relationship, and how product quality and immunogenicity affects these important properties. In addition, there is a lack of quantitative studies to support the development of pharmacokinetic-pharmacodynamic models, which can support the discovery, development, and clinical translation of this delivery system. In this review, we have provided a state-of-the-art overview of current progress and limitations related to AAV mediated delivery of protein therapeutic genes, along with our perspective on the steps that need to be taken to improve clinical translation of this therapeutic modality.

Use of Supplemented or Human Material to Simulate PD Behavior of Antibiotics at the Target Site In Vitro

In antimicrobial drug development, in vitro antibiotic susceptibility testing is conducted in standard growth media, such as Mueller–Hinton broth (MHB). These growth media provide optimal bacterial growth, but do not consider certain host factors that would be necessary to mimic the in vivo bacterial environment in the human body. The present review aimed to include relevant data published between 1986 and 2019. A database search (PubMed) was done with text keywords, such as “MIC” (minimal inhibitory concentration), “TKC” (time kill curve), “blood”, “body fluid”, “PD” (pharmacodynamic), and “in vitro”, and 53 papers were ultimately selected. Additionally, a literature search for physiologic characteristics of body fluids was conducted. This review gives an excerpt of the complexity of human compartments with their physiologic composition. Furthermore, we present an update of currently available in vitro models operated either with adapted growth media or body fluids themselves. Moreover, the feasibility of testing the activity of antimicrobials in such settings is discussed, and pro and cons for standard practice methods are given. The impact on bacterial killing varies between individual adapted microbiological media, as well as direct pharmacodynamic simulations in body fluids, between bacterial strains, antimicrobial agents, and the compositions of the adjuvants or the biological fluid itself.

Pharmacokinetics and Pharmacodynamics of (S)-Ketoprofen Co-Administered with Caffeine: A Preclinical Study in Arthritic Rats

The purpose of the present study was to determine whether caffeine modifies the pharmacokinetics and pharmacodynamics of (S)-ketoprofen following oral administration in a gout-type pain model. 3.2 mg/kg of (S)-ketoprofen alone and combined with 17.8 mg/kg of caffeine were administered to Wistar rats and plasma levels were determined between 0.5 and 24.0 h. Additionally, antinociception was evaluated based on the protocol of the PIFIR (pain-induced functional impairment in the rat) model before blood sampling between 0.5 and 4.0 h. Significant differences in C_max, AUC_0-24, and AUC_0-∞ values were observed with caffeine administration (p < 0.05). Also, significant differences in E_max, T_max, and AUC_0-4 values were determined when comparing the treatments with and without caffeine (p < 0.05). By relating the pharmacokinetic and pharmacodynamic data, a counter-clockwise hysteresis loop was observed regardless of the administration of caffeine. When the relationship between AUCe and AUCp was fitted to the sigmoidal E_max model, a satisfactory correlation was found (R² > 0.99) as well as significant differences in E_max and EC₅₀ values (p < 0.05). With caffeine, E_max and EC₅₀ values changed by 489.5% and 695.4%, respectively. The combination studied represents a convenient alternative for the treatment of pain when considering the advantages offered by using drugs with different mechanisms of action.

Influence of the pharmacokinetic profile on the plasma glucose lowering effect of the PPARγ agonist pioglitazone in Wistar fatty rats

Although the mechanism of action for peroxisome proliferator-activated receptor gamma (PPARγ) agonists has been extensively explored, the impact of the pharmacokinetic (PK) profile on the pharmacodynamic (PD) effects of PPARγ agonists has not been elucidated in detail. The importance of the PK profile of PPARγ agonist was evaluated for its PD effect based on population PK/PD analysis. Pioglitazone hydrochloride, the PPARγ agonist, was administered orally to Wistar fatty rats once a day (q.d.) or once every other day (q.2d.) as double the amount for the q.d.

TREATMENT

The plasma glucose lowering effect was selected as a surrogate PD effect for an anti-diabetic effect. The model fitting was conducted using the non-linear mixed effect modeling (NONMEM) method. The indirect response model described well the plasma glucose concentration-time profile. The q.d. treatment showed a stronger impact on the plasma glucose lowering effect than did the q.2d.

TREATMENT

The results of PK/PD modeling suggested that the sensitivity (i.e. EC₅₀ ) between each group was comparable. On the other hand, the time above the effective concentration in the q.d. treatment group was longer than that in the q.2d. treatment group. The simulation of various dose regimens suggested that the much longer exposure duration within the effective level showed a stronger plasma glucose lowering effect, even with identical exposure to pioglitazone in the plasma. The PK/PD analysis clarified that the PK profile affected the pharmacological response and that continuous exposure at an appropriate effective level would be efficient for the anti-diabetic effect of the PPARγ agonist.

Influence of covariate distribution on the predictive performance of pharmacokinetic models in paediatric research

AIMS

The accuracy of model-based predictions often reported in paediatric research has not been thoroughly characterized. The aim of this exercise is therefore to evaluate the role of covariate distributions when a pharmacokinetic model is used for simulation purposes.

METHODS

Plasma concentrations of a hypothetical drug were simulated in a paediatric population using a pharmacokinetic model in which body weight was correlated with clearance and volume of distribution. Two subgroups of children were then selected from the overall population according to a typical study design, in which pre-specified body weight ranges (10-15 kg and 30-40 kg) were used as inclusion criteria. The simulated data sets were then analyzed using non-linear mixed effects modelling. Model performance was assessed by comparing the accuracy of AUC predictions obtained for each subgroup, based on the model derived from the overall population and by extrapolation of the model parameters across subgroups.

RESULTS

Our findings show that systemic exposure as well as pharmacokinetic parameters cannot be accurately predicted from the pharmacokinetic model obtained from a population with a different covariate range from the one explored during model building. Predictions were accurate only when a model was used for prediction in a subgroup of the initial population.

CONCLUSIONS

In contrast to current practice, the use of pharmacokinetic modelling in children should be limited to interpolations within the range of values observed during model building. Furthermore, the covariate point estimate must be kept in the model even when predictions refer to a subset different from the original population.

Integration of PKPD relationships into benefit-risk analysis

AIM

Despite the continuous endeavour to achieve high standards in medical care through effectiveness measures, a quantitative framework for the assessment of the benefit-risk balance of new medicines is lacking prior to regulatory approval. The aim of this short review is to summarise the approaches currently available for benefit-risk assessment. In addition, we propose the use of pharmacokinetic-pharmacodynamic (PKPD) modelling as the pharmacological basis for evidence synthesis and evaluation of novel therapeutic agents.

METHODS

A comprehensive literature search has been performed using MESH terms in PubMed, in which articles describing benefit-risk assessment and modelling and simulation were identified. In parallel, a critical review of multi-criteria decision analysis (MCDA) is presented as a tool for characterising a drug's safety and efficacy profile.

RESULTS

A definition of benefits and risks has been proposed by the European Medicines Agency (EMA), in which qualitative and quantitative elements are included. However, in spite of the value of MCDA as a quantitative method, decisions about benefit-risk balance continue to rely on subjective expert opinion. By contrast, a model-informed approach offers the opportunity for a more comprehensive evaluation of benefit-risk balance before extensive evidence is generated in clinical practice.

CONCLUSIONS

Benefit-risk balance should be an integral part of the risk management plan and as such considered before marketing authorisation. Modelling and simulation can be incorporated into MCDA to support the evidence synthesis as well evidence generation taking into account the underlying correlations between favourable and unfavourable effects. In addition, it represents a valuable tool for the optimization of protocol design in effectiveness trials.

Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data.

A systems approach for tumor pharmacokinetics

Recent advances in genome inspired target discovery, small molecule screens, development of biological and nanotechnology have led to the introduction of a myriad of new differently sized agents into the clinic. The differences in small and large molecule delivery are becoming increasingly important in combination therapies as well as the use of drugs that modify the physiology of tumors such as anti-angiogenic treatment. The complexity of targeting has led to the development of mathematical models to facilitate understanding, but unfortunately, these studies are often only applicable to a particular molecule, making pharmacokinetic comparisons difficult. Here we develop and describe a framework for categorizing primary pharmacokinetics of drugs in tumors. For modeling purposes, we define drugs not by their mechanism of action but rather their rate-limiting step of delivery. Our simulations account for variations in perfusion, vascularization, interstitial transport, and non-linear local binding and metabolism. Based on a comparison of the fundamental rates determining uptake, drugs were classified into four categories depending on whether uptake is limited by blood flow, extravasation, interstitial diffusion, or local binding and metabolism. Simulations comparing small molecule versus macromolecular drugs show a sharp difference in distribution, which has implications for multi-drug therapies. The tissue-level distribution differs widely in tumors for small molecules versus macromolecular biologic drugs, and this should be considered in the design of agents and treatments. An example using antibodies in mouse xenografts illustrates the different in vivo behavior. This type of transport analysis can be used to aid in model development, experimental data analysis, and imaging and therapeutic agent design.

The role of population PK-PD modelling in paediatric clinical research

Children differ from adults in their response to drugs. While this may be the result of changes in dose exposure (pharmacokinetics [PK]) and/or exposure response (pharmacodynamics [PD]) relationships, the magnitude of these changes may not be solely reflected by differences in body weight. As a consequence, dosing recommendations empirically derived from adults dosing regimens using linear extrapolations based on body weight, can result in therapeutic failure, occurrence of adverse effect or even fatalities. In order to define rational, patient-tailored dosing schemes, population PK-PD studies in children are needed. For the analysis of the data, population modelling using non-linear mixed effect modelling is the preferred tool since this approach allows for the analysis of sparse and unbalanced datasets. Additionally, it permits the exploration of the influence of different covariates such as body weight and age to explain the variability in drug response. Finally, using this approach, these PK-PD studies can be designed in the most efficient manner in order to obtain the maximum information on the PK-PD parameters with the highest precision. Once a population PK-PD model is developed, internal and external validations should be performed. If the model performs well in these validation procedures, model simulations can be used to define a dosing regimen, which in turn needs to be tested and challenged in a prospective clinical trial. This methodology will improve the efficacy/safety balance of dosing guidelines, which will be of benefit to the individual child.

Do pharmacokinetic polymorphisms explain treatment failure in high-risk patients with neuroblastoma?

PURPOSE

Neuroblastoma is the most common extracranial solid tumour in childhood. It accounts for 15% of all paediatric oncology deaths. In the last few decades, improvement in treatment outcome for high-risk patients has not occurred, with an overall survival rate <30-40%. Many reasons may account for such a low survival rate. The aim of this review is to evaluate whether pharmacogenetic factors can explain treatment failure in neuroblastoma.

METHODS

A literature search based on PubMed's database Medical Subject Headings (MeSH) was performed to retrieve all pertinent publications on current treatment options and new classes of drugs under investigation. One hundred and fifty-eight articles wer reviewed, and relevant data were extracted and summarised.

RESULTS AND CONCLUSIONS

Few of the large number of polymorphisms identified thus far showed an effect on pharmacokinetics that could be considered clinically relevant. Despite their clinical relevance, none of the single nucleotide polymorphisms (SNPs) investigated can explain treatment failure. These findings seem to reflect the clinical context in which anti-tumour drugs are used, i.e. in combination with multimodal therapy. In addition, many pharmacogenetic studies did not assess (differences in) drug exposure, which could contribute to explaining pharmacogenetic associations. Furthermore, it remains unclear whether the significant activity of new drugs on different neuroblastoma cell lines translates into clinical efficacy, irrespective of resistance or myelocytomatosis viral related oncogene, neuroblastoma derived (MYCN) amplification. Elucidation of the clinical role of pharmacogenetic factors in the treatment of neuroblastoma demands an integrated pharmacokinetic-pharmacodynamic approach to the analysis of treatment response data.

Gene therapy: a pharmacokinetic/pharmacodynamic modelling overview

Since gene therapy started over 20 years ago, more than one-thousand clinical trials have been carried out. Nonviral vectors present interesting properties for their clinical application, but their efficiency in vivo is relatively low, and further improvements in these vectors are needed. Elucidating how nonviral vectors behave at the intracellular level is enlightening for vector improvement and optimization. Model-based approach is a powerful tool to understand and describe the different processes that gene transfer systems should overcome inside the body. Model-based approach allows for proposing and predicting the effect of parameter changes on the overall gene therapy response, as well as the known application of the pharmacokinetic/pharmacodynamic modelling in conventional therapies. The objective of this paper is to critically review the works in which the time-course of naked or formulated DNA have been quantitatively studied or modelled.