The virtual laboratory approach to pharmacokinetics: design principles and concepts

A STELLA simulation model for in vitro dissolution testing of respirable size particles

In vitro dissolution testing is a useful quality control tool to discriminate the formulations and to approximate the in vivo drug release profiles. A dissolution apparatus has been custom-made for dissolution testing of dry powder formulations in a small volume of stationary medium (25 μL spread over 4.91 cm² area i.e. ~50 μm thick). To understand the system and predict the key parameters which influence the dissolution of respirable size particles, a simulation model was constructed using STELLA modeling software. Using this model, the permeation (dissolution followed by diffusion through the membrane) of two anti-tubercular drugs of differing solubilities, moxifloxacin (17.68 ± 0.85 mg mL^-1) and ethionamide (0.46 ± 0.02 mg mL^-1), from the respirable size particles and their diffusion from a solution were simulated. The simulated permeation profiles of moxifloxacin from solution and respirable size particles were similar, indicating fast dissolution of the particles. However, the simulated permeation profile of ethionamide from respirable size particles showed slower permeation compared to the solution indicating the slow dissolution of the respirable size particles of ethionamide. The sensitivity analysis suggested that increased mucus volume and membrane thickness decreased the permeation of drug. While this model was useful in predicting and distinguishing the dissolution behaviours of respirable size moxifloxacin and ethionamide, further improvement could be made using appropriate initial parameter values obtained by experiments.

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework—a dynamic knowledge repository—wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline. © 2013 Wiley Periodicals, Inc.

A modern in vivo pharmacokinetic paradigm: combining snapshot, rapid and full PK approaches to optimize and expedite early drug discovery

Successful drug discovery relies on the selection of drug candidates with good in vivo pharmacokinetic (PK) properties as well as appropriate preclinical efficacy and safety profiles. In vivo PK profiling is often a bottleneck in the discovery process. In this review, we focus on the tiered in vivo PK approaches implemented at the Genomics Institute of the Novartis Research Foundation (GNF), which includes snapshot PK, rapid PK and full PK studies. These in vivo PK approaches are well integrated within discovery research, allow tremendous flexibility and are highly efficient in supporting the diverse needs and increasing demand for in vivo profiling. The tiered in vivo PK studies expedite compound profiling and help guide the selection of more desirable compounds into efficacy models and for progression into development.

Improved biological properties and hypouricemic effects of uricase from Candida utilis loaded in novel alkaline enzymosomes

Objective:

Previous studies on various enzymosomes (functional lipid vesicles encapsulating an enzyme) have been mostly carried out in vitro and have focused on preserving catalytic activity and improving the stability of the enzyme. Until now, few studies have focused on their in vivo fate. Similarly, although we have previously reported the increased in vitro uricolytic activity (about 2.2 times higher than that of free uricase, or three times higher than that of PEGylated uricase, Puricase^®, under physiological pH and temperature) and improved stability of the novel alkaline enzymosomes (functional lipid vesicles encapsulating uricase from Candida utilis: uricase-containing lipid vesicles, UOXLVs), it is still necessary to study the biological properties and hypouricemic effects of UOXLVs in vivo.

Methods:

The enzyme kinetics, pharmacokinetics, pharmacodynamics, immunogenicity, and preliminary safety of UOXLVs were evaluated.

Results:

The Michaelis constant (K_m) value of the UOXLVs was slightly lower than that of the free enzyme. The enzyme release from the UOXLVs lasted over 12 hours and their circulation half-life was about sevenfold longer than that of the free uricase. Meanwhile, the UOXLVs had a 22-fold increase in the area under the curve compared with the free uricase. Furthermore, it took less than 3 hours for the UOXLVs to lower the plasma uric acid concentration from a high to a normal level, compared with 6 hours for the free uricase. In addition, the UOXLVs had much less immunogenicity than free uricase and were well tolerated by all animals throughout the observation period.

Conclusion:

The UOXLVs markedly improved the biological properties and enhanced the hypouricemic effects of uricase in vivo.

Prediction of pharmacokinetic parameters

In silico tools specifically developed for prediction of pharmacokinetic parameters are of particular interest to pharmaceutical industry because of the high potential of discarding inappropriate molecules during an early stage of drug development itself with consequent saving of vital resources and valuable time. The ultimate goal of the in silico models of absorption, distribution, metabolism, and excretion (ADME) properties is the accurate prediction of the in vivo pharmacokinetics of a potential drug molecule in man, whilst it exists only as a virtual structure. Various types of in silico models developed for successful prediction of the ADME parameters like oral absorption, bioavailability, plasma protein binding, tissue distribution, clearance, half-life, etc. have been briefly described in this chapter.

Synthesis, in silico metabolic and toxicity prediction of some novel imidazolinones derivatives as potent anticonvulsant agents

A series of 1,2,4-trisubstituted 5-imidazolinone derivatives were synthesized by Erlenmeyer condensation of benzoylglycine (hippuric acid) with different aldehydes in the presence of sodium acetate and acetic anhydride. The derivatives of the compounds were prepared by condensation of some known sulpha drugs with 5-oxazolone derivatives. The anticonvulsant activity of the compounds was determined by the protection of pentylenetetrazole-induced convulsions that was ranged from 10 to 60%. The compounds with p-OCH₃, p-OH and o-Cl substitutions in the phenyl ring on 4(th) position of the imidazolinone ring exhibited good anticonvulsant activity. In silico metabolic and toxicity studies showed that all the compounds in the series are not likely to exhibit toxicity except the compounds IIIa, IIIb, VIa and VIb, that is predicted to show 29% mutagenicity and 53% irritation in comparison to the other compounds. The predicted lethal effect and hERG toxicity of the compounds showed that IIa, IVa, Va and Vb might be toxic at higher concentrations. The results successfully establish the synthesized imidazolinone derivatives as novel compounds with anticonvulsant properties, low predicted cardiotoxicity and lethal effects thus can be promising leads for further development as novel anticonvulsants.

A systems biology approach to dynamic modeling and inter-subject variability of statin pharmacokinetics in human hepatocytes

Background

The individual character of pharmacokinetics is of great importance in the risk assessment of new drug leads in pharmacological research. Amongst others, it is severely influenced by the properties and inter-individual variability of the enzymes and transporters of the drug detoxification system of the liver. Predicting individual drug biotransformation capacity requires quantitative and detailed models.

Results

In this contribution we present the de novo deterministic modeling of atorvastatin biotransformation based on comprehensive published knowledge on involved metabolic and transport pathways as well as physicochemical properties. The model was evaluated on primary human hepatocytes and parameter identifiability analysis was performed under multiple experimental constraints. Dynamic simulations of atorvastatin biotransformation considering the inter-individual variability of the two major involved enzymes CYP3A4 and UGT1A3 based on quantitative protein expression data in a large human liver bank (n = 150) highlighted the variability in the individual biotransformation profiles and therefore also points to the individuality of pharmacokinetics.

Conclusions

A dynamic model for the biotransformation of atorvastatin has been developed using quantitative metabolite measurements in primary human hepatocytes. The model comprises kinetics for transport processes and metabolic enzymes as well as population liver expression data allowing us to assess the impact of inter-individual variability of concentrations of key proteins. Application of computational tools for parameter sensitivity analysis enabled us to considerably improve the validity of the model and to create a consistent framework for precise computer-aided simulations in toxicology.

Hepatocytes—the choice to investigate drug metabolism and toxicity in man: In vitro variability as a reflection of in vivo

The pharmaceutical industry is committed to marketing safer drugs with fewer side effects, predictable pharmacokinetic properties and quantifiable drug-drug interactions. Drug metabolism is a major determinant of drug clearance and interindividual pharmacokinetic differences, and an indirect determinant of the clinical efficacy and toxicity of drugs. Progressive advances in the knowledge of metabolic routes and enzymes responsible for drug biotransformation have contributed to understanding the great metabolic variations existing in human beings. Phenotypic as well genotypic differences in the expression of the enzymes involved in drug metabolism are the main causes of this variability. However, only a minor part of phenotypic variability in man is attributable to gene polymorphisms, thus making the definition of a normal liver complex. At present, the use of human in vitro hepatic models at early preclinical stages means that the process of selecting drug candidates is becoming much more rational. Cultured human hepatocytes are considered to be the closest model to human liver. However, the fact that hepatocytes are located in a microenvironment that differs from that of the cell in the liver raises the question: to what extent does drug metabolism variability observed in vitro actually reflect that of the liver in vivo? By comparing the metabolism of a model compound both in vitro and in vivo in the same individual, a good correlation between the in vitro and in vivo relative abundance of oxidized metabolites and the hydrolysis of the compound was observed. Thus, it is reasonable to consider that the variability observed in human hepatocytes reflects the existing phenotypic heterogeneity of the P450 expression in human liver.

Computational systems biology in drug discovery and development: methods and applications

Computational systems biology is an emerging field in biological simulation that attempts to model or simulate intra- and intercellular events using data gathered from genomic, proteomic or metabolomic experiments. The need to model complex temporal and spatiotemporal processes at many different scales has led to the emergence of numerous techniques, including systems of differential equations, Petri nets, cellular automata simulators, agent-based models and pi calculus. This review provides a brief summary and an assessment of most of these approaches. It also provides examples of how these methods are being used to facilitate drug discovery and development.