Pharmacodynamic Modelling of Biomarker Data in Oncology

The development of pharmacodynamic (PD) biomarkers in oncology has implications for design of clinical protocols from preclinical data and for predicting clinical outcomes from early clinical data. Two classes of biomarkers have received particular attention. Phosphoproteins in biopsy samples are markers of inhibition of signalling pathways, target sites for many novel agents. Biomarkers of apoptosis in plasma can measure tumour cell killing by drugs in phase I clinical trials. The predictive power of PD biomarkers is enhanced by data modelling. With pharmacokinetic models, PD models form PK/PD models that predict the time course both of drug concentration and drug effects. If biomarkers of drug toxicity are also measured, the models can predict drug selectivity as well as efficacy. PK/PD models, in conjunction with disease models, make possible virtual clinical trials, in which multiple trial designs are assessed in silico, so the optimal trial design can be selected for experimental evaluation

Optimizing antimicrobial treatment schedules: some fundamental analytical results

This work studies fundamental questions regarding the optimal design of antimicrobial treatment protocols, using standard pharmacodynamic and pharmacokinetic mathematical models. We consider the problem of designing an antimicrobial treatment schedule to achieve eradication of a microbial infection, while minimizing the area under the time-concentration curve (AUC). We first solve this problem under the assumption that an arbitrary antimicrobial concentration profile may be chosen, and prove that the 'ideal' concentration profile consists of a constant concentration over a finite time duration, where explicit expressions for the optimal concentration and the time duration are given in terms of the pharmacodynamic parameters. Since antimicrobial concentration profiles are induced by a dosing schedule and the antimicrobial pharmacokinetics, the ideal concentration profile is not strictly feasible. We therefore also investigate the possibility of achieving outcomes which are close to those provided by the ideal concentration profile,using a bolus+continuous dosing schedule, which consists of a loading dose followed by infusion of the antimicrobial at a constant rate. We explicitly find the optimal bolus+continuous dosing schedule, and show that, for realistic parameter ranges, this schedule achieves results which are nearly as efficient as those attained by the ideal concentration profile. The optimality results obtained here provide a baseline and reference point for comparison and evaluation of antimicrobial treatment plans

Advanced multiparametric optimization and control studies for anaesthesia

Anaesthesia is a reversible pharmacological state of the patient where hypnosis, analgesia and muscle relaxation are guaranteed and maintained throughout the surgery. Analgesics block the sensation of pain; hypnotics produce unconsciousness, while muscle relaxants prevent unwanted movement of muscle tone. Controlling the depth of anaesthesia is a very challenging task, as one has to deal with nonlinearity, inter- and intra-patient variability, multivariable characteristics, variable time delays, dynamics dependent on the hypnotic agent, model analysis variability, agent and stability issues. The modelling and automatic control of anaesthesia is believed to (i) benefit the safety of the patient undergoing surgery as side-effects may be reduced by optimizing the drug infusion rates, and (ii) support anaesthetists during critical situations by automating the drug delivery systems. In this work we have developed several advanced explicit/multi-parametric model predictive (mp-MPC) control strategies for the control of depth of anaesthesia. State estimation techniques are developed and used simultaneously with mp-MPC strategies to estimate the state of each individual patient, in an attempt to overcome the challenges of inter- and intra- patient variability, and deal with possible unmeasurable noisy outputs. Strategies to deal with the nonlinearity have been also developed including local linearization, exact linearization as well as a piece-wise linearization of the Hill curve leading to a hybrid formulation of the patient model and thereby the development of multiparametric hybrid model predictive control methodology. To deal with the inter- and intra- patient variability, as well as the noise on the process output, several robust techniques and a multiparametric moving horizon estimation technique have been design and implemented. All the studies described in the thesis are performed on clinical data for a set of 12 patients who underwent general anaesthesia.Open Acces

Can a microscopic stochastic model explain the emergence of pain cycles in patients?

A stochastic model is here introduced to investigate the molecular mechanisms which trigger the perception of pain. The action of analgesic drug compounds is discussed in a dynamical context, where the competition with inactive species is explicitly accounted for. Finite size effects inevitably perturb the mean-field dynamics: Oscillations in the amount of bound receptors spontaneously manifest, driven by the noise which is intrinsic to the system under scrutiny. These effects are investigated both numerically, via stochastic simulations and analytically, through a large-size expansion. The claim that our findings could provide a consistent interpretative framework to explain the emergence of cyclic behaviors in response to analgesic treatments, is substantiated.Comment: J. Stat. Mech. (Proceedings UPON2008

Guidelines for the recording and evaluation of pharmaco-EEG data in man: the International Pharmaco-EEG Society (IPEG)

The International Pharmaco-EEG Society (IPEG) presents updated guidelines summarising the requirements for the recording and computerised evaluation of pharmaco-EEG data in man. Since the publication of the first pharmaco-EEG guidelines in 1982, technical and data processing methods have advanced steadily, thus enhancing data quality and expanding the palette of tools available to investigate the action of drugs on the central nervous system (CNS), determine the pharmacokinetic and pharmacodynamic properties of novel therapeutics and evaluate the CNS penetration or toxicity of compounds. However, a review of the literature reveals inconsistent operating procedures from one study to another. While this fact does not invalidate results per se, the lack of standardisation constitutes a regrettable shortcoming, especially in the context of drug development programmes. Moreover, this shortcoming hampers reliable comparisons between outcomes of studies from different laboratories and hence also prevents pooling of data which is a requirement for sufficiently powering the validation of novel analytical algorithms and EEG-based biomarkers. The present updated guidelines reflect the consensus of a global panel of EEG experts and are intended to assist investigators using pharmaco-EEG in clinical research, by providing clear and concise recommendations and thereby enabling standardisation of methodology and facilitating comparability of data across laboratories

Linked Pharmacometric-Pharmacoeconomic Modeling and Simulation in Clinical Drug Development

Market access and pricing of pharmaceuticals are increasingly contingent on the ability to demonstrate comparative effectiveness and cost-effectiveness. As such, it is widely recognized that predictions of the economic potential of drug candidates in development could inform decisions across the product life cycle. This may be challenging when safety and efficacy profiles in terms of the relevant clinical outcomes are unknown or highly uncertain early in product development. Linking pharmacometrics and pharmacoeconomics, such that outputs from pharmacometric models serve as inputs to pharmacoeconomic models, may provide a framework for extrapolating from early-phase studies to predict economic outcomes and characterize decision uncertainty. This article reviews the published studies that have implemented this methodology and used simulation to inform drug development decisions and/or to optimize the use of drug treatments. Some of the key practical issues involved in linking pharmacometrics and pharmacoeconomics, including the choice of final outcome measures, methods of incorporating evidence on comparator treatments, approaches to handling multiple intermediate end points, approaches to quantifying uncertainty, and issues of model validation are also discussed. Finally, we have considered the potential barriers that may have limited the adoption of this methodology and suggest that closer alignment between the disciplines of clinical pharmacology, pharmacometrics, and pharmacoeconomics, may help to realize the potential benefits associated with linked pharmacometric-pharmacoeconomic modeling and simulation

Prevention of Organ-Specific Doxorubicin Induced Toxicity Using Physiologically-Based Pharmacokinetic Modeling and Therapeutic Drug Monitoring

Physiology-based pharmacokinetic models are mathematical models that characterize the behavior of a drug and have compartmental equations that are representative of specific tissues and physiological processes.[1, 2] Doxorubicin is an anthracycline antibiotic that is effective and widely used in anticancer therapy due to its potent cytotoxicity. Unfortunately, with that potency comes cardiotoxic side effects related to cumulative lifetime dose.[3] Specifically, the toxicity is related to the accumulation of the primary metabolite doxorubicinol (DOXol) in the heart.[4] Since the toxicity is organ-specific, the best way to characterize the behavior is through PBPK modeling.[2] Since PBPK models tend to be large systems of ODEs, several numerical methods were attempted for solving the model before a matrix-based approach was chosen.[5, 6] The eigenvalue/eigenvector solution was evaluated at three time points which were then included in a Composite Simpson’s Rule numerical integration for the length of some time interval.[5, 7] The PBPK model, adapted from a pig model, was fit to mouse data and scaled to predict rat, rabbit, dog, pig, and human data sets using an allometric scaling equation on the blood:plasma partition coefficient B : P .[8, 9, 10] Despite extensive investigation into dose adjustments for DOX, no covariates were consistently found to improve the efficacy and minimize toxicity except dosing schedule – infusion rate and duration.[11] The criterion for decreasing incidence of cardiotoxicity was maintaining a sub-toxic Cmax,heart,DOXol in the heart while maximizing exposure, represented by area under the concentration-time-curve (AUC). Thus, the original mouse data set was ideal since it included both DOX venous blood concentration and DOXol heart concentration.[12] The model was optimized at 10 time points between 1 minute and 72 hours with the goal of (AUC) maximization without exceeding Cmax,heart,DOXol. Using these predictions, therapeutic drug monitoring could be executed by taking the plasma concentration samples during a patient’s first DOX dose, PBPK model predictions could provide AUC and Cmax,heart,DOXol data, which could then inform the infusion parameters for the next dose. Clinical thresholds for Cmax,vb have been established for incidence of adverse effects, and in future work, perhaps a similar threshold for cardiotoxicity could also be established using tissue-specific measures