Simulation models for drug disposition and drug interactions

A Biophysical Insight of Camptothecin Biodistribution: Towards a Molecular Understanding of Its Pharmacokinetic Issues

Camptothecin (CPT) is a potent anticancer drug, and its putative oral administration is envisioned although difficult due to physiological barriers that must be overcome. A comprehensive biophysical analysis of CPT interaction with biointerface models can be used to predict some pharmacokinetic issues after oral administration of this or other drugs. To that end, different models were used to mimic the phospholipid composition of normal, cancer, and blood–brain barrier endothelial cell membranes. The logD values obtained indicate that the drug is well distributed across membranes. CPT-membrane interaction studies also confirm the drug’s location at the membrane cooperative and interfacial regions. The drug can also permeate membranes at more ordered phases by altering phospholipid packing. The similar logD values obtained in membrane models mimicking cancer or normal cells imply that CPT has limited selectivity to its target. Furthermore, CPT binds strongly to serum albumin, leaving only 8.05% of free drug available to be distributed to the tissues. The strong interaction with plasma proteins, allied to the large distribution (VD_SS = 5.75 ± 0.932 L·Kg⁻¹) and tendency to bioaccumulate in off-target tissues, were predicted to be pharmacokinetic issues of CPT, implying the need to develop drug delivery systems to improve its biodistribution.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Prediction of paclitaxel pharmacokinetic based on in vitro studies: Interaction with membrane models and human serum albumin

Interactions of paclitaxel (PTX) with models mimicking biological interfaces (lipid membranes and serum albumin, HSA) were investigated to test the hypothesis that the set of in vitro assays proposed can be used to predict some aspects of drug pharmacokinetics (PK). PTX membrane partitioning was studied by derivative spectrophotometry; PTX effect on membrane biophysics was evaluated by dynamic light scattering, fluorescence anisotropy, atomic force microscopy and synchrotron small/wide-angle X-ray scattering; PTX distribution/molecular orientation in membranes was assessed by steady-state/time-resolved fluorescence and computer simulations. PTX binding to HSA was studied by fluorescence quenching, derivative spectrophotometry and dynamic/electrophoretic light scattering. PTX high membrane partitioning is consistent with its efficacy crossing cellular membranes and its off-target distribution. PTX is closely located in the membrane phospholipids headgroups, also interacting with the hydrophobic chains, and causes a major distortion of the alignment of the membrane phospholipids, which, together with its fluidizing effect, justifies some of its cellular toxic effects. PTX binds strongly to HSA, which is consistent with its reduced distribution in target tissues and toxicity by bioaccumulation. In conclusion, the described set of biomimetic models and techniques has the potential for early prediction of PK issues, alerting for the required drug optimizations, potentially minimizing the number of animal tests used in the drug development process.

PBPK and its Virtual Populations: the Impact of Physiology on Pediatric Pharmacokinetic Predictions of Tramadol

In pediatric PBPK models, age-related changes in the body are known to occur. Given the sparsity of and the variability associated with relevant physiological parameters, different PBPK software providers may vary in their system's data. In this work, three commercially available PBPK software packages (PK-Sim®, Simcyp®, and Gastroplus®) were investigated regarding their differences in system-related information, possibly affecting clearance prediction. Three retrograde PBPK clearance models were set up to enable prediction of pediatric tramadol clearance. These models were qualified in terms of total, CYP2D6, and renal clearance in adults. Tramadol pediatric clearance predictions from PBPK were compared with a pooled popPK model covering clearance ranging from neonates to adults. Fold prediction errors were used to evaluate the results. Marked differences in liver clearance prediction between PBPK models were observed. In general, the prediction bias of total clearance was greatest at the youngest population and decreased with age. Regarding CYP2D6 and renal clearance, important differences exist between PBPK software tools. Interestingly, the PBPK model with the shortest CYP2D6 maturation half-life (PK-Sim) agreed best with the in vivo CYP2D6 maturation model. Marked differences in physiological data explain the observed differences in hepatic clearance prediction in early life between the various PBPK software providers tested. Consensus on the most suited pediatric data to use should harmonize and optimize pediatric clearance predictions. Moreover, the combination of bottom-up and top-down approaches, using a convenient probe substrate, has the potential to update system-related parameters in order to better represent pediatric physiology.

Novel pre-clinical methodologies for pharmacokinetic drug-drug interaction studies: spotlight on "humanized" animal models

Poly-therapy is common due to co-occurrence of several ailments in patients, leading to the elevated possibility of drug-drug interactions (DDI). Pharmacokinetic DDI often accounts for severe adverse drug reactions in patients resulting in withdrawal of drug from the market. Hence, the prediction of DDI is necessary at pre-clinical stage of drug development. Several human tissue and cell line-based in vitro systems are routinely used for screening metabolic and transporter pathways of investigational drugs and for predicting their clinical DDI potentials. However, ample constraints are associated with the in vitro systems and sometimes in vitro-in vivo extrapolation (IVIVE) fail to assess the risk of DDI in clinic. In vitro-in vivo correlation model in animals combined with human in vitro studies may be helpful in better prediction of clinical outcome. Native animal models vary remarkably from humans in drug metabolizing enzymes and transporters, hence, the interpretation of results from animal DDI studies is difficult. With the advent of modern molecular biology and engineering tools, novel pre-clinical animal models, namely, knockout rat/mouse, transgenic rat/mouse with humanized drug metabolizing enzymes and/or transporters and chimeric rat/mouse with humanized liver are developed. These models nearly simulate human-like drug metabolism and help to validate the in vivo relevance of the in vitro human DDI data. This review briefly discusses the application of such novel pre-clinical models for screening various type of DDI along with their advantages and limitations.

Physiology-based pharmacokinetic modeling: ready to be used

Physiology-based pharmacokinetic (PBPK) modeling is well recognized as a technology for mechanistically simulating and predicting the fate of substances in a mammalian body. Today, the demand for this methodology is higher than ever. The pharma industry and regulatory agencies are looking for new methods, which help to speed up and increase the efficiency of the development process for new drugs. Implementing PBPK modeling in the drug research and development workflow contributes significantly to reach this goal.:

Mining clinical text for signals of adverse drug-drug interactions

BACKGROUND AND OBJECTIVE

Electronic health records (EHRs) are increasingly being used to complement the FDA Adverse Event Reporting System (FAERS) and to enable active pharmacovigilance. Over 30% of all adverse drug reactions are caused by drug-drug interactions (DDIs) and result in significant morbidity every year, making their early identification vital. We present an approach for identifying DDI signals directly from the textual portion of EHRs.

METHODS

We recognize mentions of drug and event concepts from over 50 million clinical notes from two sites to create a timeline of concept mentions for each patient. We then use adjusted disproportionality ratios to identify significant drug-drug-event associations among 1165 drugs and 14 adverse events. To validate our results, we evaluate our performance on a gold standard of 1698 DDIs curated from existing knowledge bases, as well as with signaling DDI associations directly from FAERS using established methods.

RESULTS

Our method achieves good performance, as measured by our gold standard (area under the receiver operator characteristic (ROC) curve >80%), on two independent EHR datasets and the performance is comparable to that of signaling DDIs from FAERS. We demonstrate the utility of our method for early detection of DDIs and for identifying alternatives for risky drug combinations. Finally, we publish a first of its kind database of population event rates among patients on drug combinations based on an EHR corpus.

CONCLUSIONS

It is feasible to identify DDI signals and estimate the rate of adverse events among patients on drug combinations, directly from clinical text; this could have utility in prioritizing drug interaction surveillance as well as in clinical decision support.

Drug-drug interaction through molecular structure similarity analysis

BACKGROUND

Drug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but is very challenging. Currently, the US Food and Drug Administration and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs.

METHODS

We present a new methodology applicable on a large scale that identifies novel DDIs based on molecular structural similarity to drugs involved in established DDIs. The underlying assumption is that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. DrugBank was used as a resource for collecting 9454 established DDIs. The structural similarity of all pairs of drugs in DrugBank was computed to identify DDI candidates.

RESULTS

The methodology was evaluated using as a gold standard the interactions retrieved from the initial DrugBank database. Results demonstrated an overall sensitivity of 0.68, specificity of 0.96, and precision of 0.26. Additionally, the methodology was also evaluated in an independent test using the Micromedex/Drugdex database.

CONCLUSION

The proposed methodology is simple, efficient, allows the investigation of large numbers of drugs, and helps highlight the etiology of DDI. A database of 58 403 predicted DDIs with structural evidence is provided as an open resource for investigators seeking to analyze DDIs.

A simulation model for the prediction of tissue:plasma partition coefficients for drug residues in natural casings

Tissue residues arise from the exposure of animals to undesirable substances in animal feed materials and drinking water and to the therapeutic or zootechnical use of veterinary medicinal products. In the framework of this study, an advanced toxicokinetic model was developed to predict the likelihood of residue disposition of licensed veterinary products in natural casings used as envelope for a variety of meat products, such as sausages. The model proved suitable for the calculation of drug concentrations in the muscles of pigs, cattle and sheep, the major species of which intestines are used. On the basis of drug concentrations in muscle tissue, the model allowed a prediction of intestinal concentrations and residues in the intestines that remained equal to or below the concentrations in muscle tissue, the major consumable product of slaughter animals. Subsequently, residues in intestines were found to be below the maximum residue limit value for muscle tissue when drugs were used according to prescribed procedures, including the application of appropriate withdrawal times. Considering the low consumption of natural casings (which represents only about 1-2% of the weight of a normal sausage), it was concluded that the exposure to drug residues from casings is negligible.

Predicting drug-drug interactions: an FDA perspective

Pharmacokinetic drug interactions can lead to serious adverse events, and the evaluation of a new molecular entity's drug-drug interaction potential is an integral part of drug development and regulatory review prior to its market approval. Alteration of enzyme and/or transporter activities involved in the absorption, distribution, metabolism, or excretion of a new molecular entity by other concomitant drugs may lead to a change in exposure leading to altered response (safety or efficacy). Over the years, various in vitro methodologies have been developed to predict drug interaction potential in vivo. In vitro study has become a critical first step in the assessment of drug interactions. Well-executed in vitro studies can be used as a screening tool for the need for further in vivo assessment and can provide the basis for the design of subsequent in vivo drug interaction studies. Besides in vitro experiments, in silico modeling and simulation may also assist in the prediction of drug interactions. The recent FDA draft drug interaction guidance highlighted the in vitro models and criteria that may be used to guide further in vivo drug interaction studies and to construct informative labeling. This report summarizes critical elements in the in vitro evaluation of drug interaction potential during drug development and uses a case study to highlight the impact of in vitro information on drug labeling.

Development and application of physiologically based pharmacokinetic-modeling tools to support drug discovery

Physiologically based pharmacokinetic (PBPK) modeling integrates physicochemical (PC) and in vitro pharmacokinetic (PK) data using a mechanistic framework of principal ADME (absorption, distribution, metabolism, and excretion) processes into a physiologically based whole-body model. Absorption, distribution, and clearance are modeled by combining compound-specific PC and PK properties with physiological processes. Thereby, isolated in vitro data can be upgraded by means of predicting full concentration-time profiles prior to animal experiments. The integrative process of PBPK modeling leads to a better understanding of the specific ADME processes driving the PK behavior in vivo, and has the power to rationally select experiments for a more focussed PK project support. This article presents a generic disposition model based on tissue-composition-based distribution and directly scaled hepatic clearance. This model can be used in drug discovery to identify the critical PK issues of compound classes and to rationally guide the optimization path of the compounds toward a viable development candidate. Starting with a generic PBPK model, which is empirically based on the most common PK processes, the model will be gradually tailored to the specifics of drug candidates as more and more experimental data become available. This will lead to a growing understanding of the 'drug in the making', allowing a range of predictions to be made for various purposes and conditions. The stage is set for a wide penetration of PK modeling and simulations to form an intrinsic part of a project starting from lead discovery, to lead optimization and candidate selection, to preclinical profiling and clinical trials.

High throughput microsomal stability assay for insoluble compounds

High throughput metabolic stability assays are widely implemented in drug discovery to guide structural modification, predict in vivo performance, develop structure-metabolic stability relationships, and triage compounds for in vivo animal studies. However, these methods are often developed and validated using commercial drugs. Many drug discovery compounds differ from commercial drugs, with many having high lipophilicity, high molecular weight and low solubility. The impact of very low solubility on metabolic stability assay results was explored. Two metabolic stability assays, the 'aqueous dilution method' and the 'cosolvent method, were compared. For commercial drugs and most discovery compounds having reasonable drug-like properties, the two methods gave comparable results. For highly lipophilic, insoluble drug discovery compounds, the 'aqueous dilution method' gave artificially higher stability results. The cosolvent method performs compound dilutions in solutions with higher organic solvent content and adds solutions directly to microsomes to assist with solubilization, minimize precipitation and reduce non-specific binding to plastics. This method is more applicable in drug discovery where compounds of a wide range of solubility are studied.