Kinetics of drug action in disease states: towards physiology-based pharmacodynamic (PBPD) models

Betulinic acid exerts antigenotoxic and anticarcinogenic activities via inhibition of COX-2 and PCNA in rodents

Phytochemicals have been suggested as an effective strategy for cancer prevention. Within this context, triterpene betulinic acid (BA) exhibits several biological properties but its chemopreventive effect has not been fully demonstrated. The present study investigated the antigenotoxic potential of BA against doxorubicin (DXR)-induced genotoxicity using the mouse peripheral blood micronucleus assay, as well as its anticarcinogenic activity against 1,2dimethylhydrazine (DMH)-induced colorectal lesions in rats. Micronuclei (MN) assay and aberrant crypt foci assay were used to assess the antigenotoxic and the anticarcinogenic potential, respectively. The molecular mechanisms underlying the anticarcinogenic activity of BA were evaluated by assessing anti-inflammatory (COX-2) and antiproliferative (PCNA) pathways. The results demonstrated that BA at the dose of 0.5 mg/kg bodyweight exerted antigenotoxic effects against DXR, with a reduction of 70.2% in the frequencies of chromosomal damage. Animals treated with BA showed a 64% reduction in the number of preneoplastic lesions when compared to those treated with the carcinogen alone. The levels of COX-2 and PCNA expression in the colon were significantly lower in animals treated with BA and DMH compared to those treated with the carcinogen alone. The chemopreventive effect of BA is related, at least in part, to its antiproliferative and anti-inflammatory activity, indicating a promising potential of this triterpene in anticancer therapies, especially for colorectal cancer.

Current Approaches and Techniques in Physiologically Based Pharmacokinetic (PBPK) Modelling of Nanomaterials

There have been efforts to develop physiologically based pharmacokinetic (PBPK) models for nanomaterials (NMs). Since NMs have quite different kinetic behaviors, the applicability of the approaches and techniques that are utilized in current PBPK models for NMs is warranted. Most PBPK models simulate a size-independent endocytosis from tissues or blood. In the lungs, dosimetry and the air-liquid interface (ALI) models have sometimes been used to estimate NM deposition and translocation into the circulatory system. In the gastrointestinal (GI) tract, kinetics data are needed for mechanistic understanding of NM behavior as well as their absorption through GI mucus and their subsequent hepatobiliary excretion into feces. Following absorption, permeability (P_t) and partition coefficients (PCs) are needed to simulate partitioning from the circulatory system into various organs. Furthermore, mechanistic modelling of organ- and species-specific NM corona formation is in its infancy. More recently, some PBPK models have included the mononuclear phagocyte system (MPS). Most notably, dissolution, a key elimination process for NMs, is only empirically added in some PBPK models. Nevertheless, despite the many challenges still present, there have been great advances in the development and application of PBPK models for hazard assessment and risk assessment of NMs.

Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap

Despite the application of advanced statistical and pharmacometric approaches to pediatric trial data, a large pediatric evidence gap still remains. Here, we discuss how to collect more data from children by using real-world data from electronic health records, mobile applications, wearables, and social media. The large datasets collected with these approaches enable and may demand the use of artificial intelligence and machine learning to allow the data to be analyzed for decision making. Applications of this approach are presented, which include the prediction of future clinical complications, medical image analysis, identification of new pediatric end points and biomarkers, the prediction of treatment nonresponders, and the prediction of placebo-responders for trial enrichment. Finally, we discuss how to bring machine learning from science to pediatric clinical practice. We conclude that advantage should be taken of the current opportunities offered by innovations in data science and machine learning to close the pediatric evidence gap.

[Pharmacokinetic and pharmacodynamic modelling in paediatric drug development with a focus on physiology-based pharmacokinetic simulations]

Pharmacokinetic and pharmacodynamic models are mandatory for dosing and the safe use of drugs in the paediatric population. Different modelling methods allow for the development of dosing regimens for children requiring only a small number of blood samples or none at all. The medicines regulatory authorities recommend using these methods for paediatric drug development programs. Taking sildenafil as an example, the least invasive method of physiology-based pharmacokinetic simulation for the development of dosing regimens in the paediatric population is presented.

Opioid-induced respiratory depression in humans: a review of pharmacokinetic-pharmacodynamic modelling of reversal

BACKGROUND

Opioids are potent painkillers but come with serious adverse effects ranging from addiction to potentially lethal respiratory depression. A variety of drugs with separate mechanisms of action are available to prevent or reverse opioid-induced respiratory depression (OIRD).

METHODS

The authors reviewed human studies on reversal of OIRD using models that describe and predict the time course of pharmacokinetics (PK) and pharmacodynamics (PD) of opioids and reversal agents and link PK to PD.

RESULTS

The PKPD models differ in their basic structure to capture the specific pharmacological mechanisms by which reversal agents interact with opioid effects on breathing. The effect of naloxone, a competitive opioid receptor antagonist, is described by the combined effect-compartment receptor-binding model to quantify rate limitation at the level of drug distribution and receptor kinetics. The effects of reversal agents that act through non-opioidergic pathways, such as ketamine and the experimental drug GAL021, are described by physiological models, in which stimulants act at CO₂ chemosensitivity, CO₂-independent ventilation, or both. The PKPD analyses show that although all reversal strategies may be effective under certain circumstances, there are conditions at which reversal is less efficacious and sometimes even impossible.

CONCLUSIONS

Model-based drug development is needed to design an 'ideal' reversal agent-that is, one that is not influenced by opioid receptor kinetics, does not interfere with opioid analgesia, has a rapid onset of action with long-lasting effects, and is devoid of adverse effects.

How are growth hormone and insulin-like growth factor-1 reported as markers for drug effectiveness in clinical acromegaly research? A comprehensive methodologic review

OBJECTIVE

In rare disease research, most randomized prospective clinical trials can only use limited number of patients and are comprised of highly heterogeneous populations. Therefore, it is crucial to report the results in such a manner that it allows for comparison of treatment effectiveness and biochemical control between studies. The aim of this review was to investigate the current methods that are being applied to measure and report growth hormone (GH) and insulin-like growth factor-1 (IGF-1) as markers for drug effectiveness in clinical acromegaly research.

SEARCH STRATEGY

A systematic search of recent prospective and retrospective studies, published between 2012 and 2017, that studied the effects of somatostatin analogues or dopamine agonists in acromegaly patients was performed. The markers of interest were GH, IGF-1, and the suppression of GH after an oral glucose tolerance test (OGTT). Additionally, the use of pharmacokinetic (PK) measurements in these studies was analyzed. The sampling design, cut-off for biochemical control, reported units, and used summary statistics were summarized.

RESULTS

A total of 49 articles were selected out of the 263 screened abstracts. IGF-1 concentrations were measured in all 49 studies, GH in 45 studies, and an OGTT was performed in 11 studies. A wide range of different cut-off values and sampling designs were used to determine biochemical control in acromegaly patients. The summary statistics were reported in various ways, with the percentage of biochemical control most frequently used. Nine studies sampled the PK at one or more time points. Non-compartmental analyses were commonly performed on the available PK data.

CONCLUSIONS

The way GH and IGF-1 are measured and reported in acromegaly research varies considerably. A consensus on how to report study results would enable better comparisons between studies, thereby improving evidence based decision making to optimize treatment in acromegaly.

Impact of pharmacokinetic-pharmacodynamic modelling in early clinical drug development

Early clinical pharmacology studies in healthy subjects are often dissociated from patient studies. In this review we encourage the use of modelling and simulation techniques to generate valuable information at an early stage of clinical development. We illustrate these principles by presenting 5 different case studies from a spectrum of therapeutic drug classes. Their application leads to a better understanding of drug characteristics early on, thereby facilitating the design of dose-finding studies in the target patient population and saving resources.

Data Sharing in the Pharmaceutical Enterprise: The Genie's Out of the Bottle

OBJECTIVE

This Commentary shows that the present emphasis on the sharing of data from clinical trials can be extended to the entire pharmaceutical enterprise.

METHODS

The authors constructed a Data Sharing Dashboard that shows the relationship between all of the life-cycle domains of the pharmaceutical enterprise from discovery to obsolescence and the domain-bridging disciplines, such as target credentialing, structure-activity relationships, and exposure-effect relationships.

FINDINGS

The published literature encompassing the pharmaceutical enterprise is expansive, covering the major domains of discovery, translation, clinical development, and post-marketing outcomes research, all of which have even larger, though generally inaccessible, troves of legacy data bases. Notable exceptions include the fields of genomics and bioinformatics.

IMPLICATIONS

We have the opportunity to broaden the present momentum of interest in data sharing to the entire pharmaceutical enterprise, beginning with discovery and extending into health technology assessment and post-patent expiry generic use with the plan of integrating new levels and disciplines of knowledge and with the ultimate goal of improving the care of our patients.

Systems pharmacology - Towards the modeling of network interactions

Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior. Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders). In this contribution it is shown how physiology-based PKPD and disease models can be extended to account for internal systems interactions. It is demonstrated how SP models can be used to predict the effects of multi-target interactions and of homeostatic feedback on the pharmacological response. In addition it is shown how DS models may be used to distinguish symptomatic from disease modifying effects and to predict the long term effects on disease progression, from short term biomarker responses. It is concluded that incorporation of expressions to describe the interactions in biological network analysis opens new avenues to the understanding of the effects of drug treatment on the fundamental aspects of biological systems behavior.