Pharmacokinetic software for the health sciences: choosing the right package for teaching purposes

SAAM II: A general mathematical modeling rapid prototyping environment

Simulation Analysis and Modeling II (SAAM II) is a graphical modeling software used in life sciences for compartmental model analysis, particularly, but not exclusively, appreciated in pharmacokinetics (PK) and pharmacodynamics (PD), metabolism, and tracer modeling. Its intuitive "circles and arrows" visuals allow users to easily build, solve, and fit compartmental models without the need for coding. It is suitable for rapid prototyping of models for complex kinetic analysis or PK/PD problems, and in educating students and non-modelers. Although it is straightforward in design, SAAM II incorporates sophisticated algorithms programmed in C to address ordinary differential equations, deal with complex systems via forcing functions, conduct multivariable regression featuring the Bayesian maximum a posteriori, perform identifiability and sensitivity analyses, and offer reporting functionalities, all within a single package. After 26 years from the last SAAM II tutorial paper, we demonstrate here SAAM II's updated applicability to current life sciences challenges. We review its features and present four contemporary case studies, including examples in target-mediated PK/PD, CAR-T-cell therapy, viral dynamics, and transmission models in epidemiology. Through such examples, we demonstrate that SAAM II provides a suitable interface for rapid model selection and prototyping. By enabling the fast creation of detailed mathematical models, SAAM II addresses a unique requirement within the mathematical modeling community.

Multi-channel Small Animal Drug Metabolism Real-Time Monitoring Fluorescence System

PURPOSE

The data acquisition of drug metabolism analysis requires a lot of time and animal resources. However, there are often many deviations in the results of pharmacokinetic analysis. Conventional methods cannot measure the blood drug concentration data in multiple tissues at the same time, and the data is obtained by in vitro measurement, which produces time errors, in vitro data errors, and individual differences between animals. In the analysis of pharmacokinetic parameters, it will seriously affect the pass rate of clinical trials of R&D drugs and the accuracy of the dosing schedule. To the best of our knowledge, we have not found the study of in vivo blood drug concentration using multi-channel equipment. Therefore, the purpose of this paper is to build a set of multi-organ monitoring and analysis instruments for synchronously monitoring the metabolism of drugs in various tissues of small animals, so as to obtain real in vivo data of blood drug concentration in real time.

PROCEDURES

Using the fluorescence properties and laser-induced fluorescence principle of drugs, we designed six channels to monitor the changes of fluorescence-labeled drugs in their main metabolic organs, a multi-channel calibration method was proposed to improve the accuracy of the time-division multiplexing, the real-time collection of drug concentration in vivo is realized, and the drug metabolism curve in vivo can be observed.

RESULTS

The instrument satisfies the collection of small doses of drugs such as microgram; the detection sensitivity can reach 10 ng/ml, and can monitor and collect the drug metabolism of multiple small animal tissues at the same time, which greatly reduces the use of animals, reduces the differences between individuals, and reduces consumption cost and improve the detection efficiency of parameters, and obtain data information that is closer to the real biology.

CONCLUSION

The real-time continuous monitoring and data collection of the drug metabolism in the plasma of living small animals and the important organs such as kidney, liver, and spleen were realized. The research and development of new drugs and clinical research have higher practical value.

Teaching of drug disposition using physiologically based pharmacokinetic modeling software: GastroPlus as an educational tool

Physiologically based pharmacokinetic (PBPK) modeling requires an understanding of chemical, physiologic, and pharmacokinetic principles. Active learning with PBPK modeling software (GastroPlus) may be useful to teach these scientific principles while also teaching software operation. To examine this issue, a graduate-level course was designed using learning objectives in science, software use, and PBPK model application. These objectives were taught through hands-on PBPK modeling to answer clinically relevant questions. Students demonstrated proficient use of software, based on their responses to these questions, and showed an improved understanding of scientific principles on a pre- and post-course assessment. These outcomes support the effectiveness of simultaneous teaching of interdependent software and science.NEW & NOTEWORTHY Physiologically based pharmacokinetic (PBPK) modeling is a major growth area in drug development, regulatory submissions, and clinical applications. There is a demand for experts in this area with multidisciplinary backgrounds. In this article, we describe a course designed to teach PBPK modeling and the underlying scientific principles in parallel.

Tamoxifen in horses: pharmacokinetics and safety study

Background

Tamoxifen (TAM), a selective modulator of estrogen receptors (SERMs) has been recently explored as a therapeutic option for the oral treatment of airway inflammation in the horse. The objective of this work was to establish pharmacokinetic parameters of TAM and its main metabolites in equines, as well as to determine its clinical safety in short-term treatments.

Results

We determined TAM and its three main metabolites (4-OH tamoxifen, endoxifen, and N-desmethyl tamoxifen) in plasma after single administration of 0.25 mg/kg in healthy adult horses (n = 12). A maximum concentration of TAM was achieved 3 h after the oral administration (4.65 pg/mL ± 1.69); 4-OH tamoxifen was the metabolite that reached the highest concentration (78 pg/mL ± 70), followed by N-desmethyl tamoxifen (0.43 pg / mL ± 0.48), and finally endoxifen (0.17 pg/mL ± 0.17). All metabolites showed peak concentration 2 h after oral administration of the drug. Oral TAM bioavailability was 13,15% ± 4,18, with a steady state volume of distribution of 7831 ± 2922 (L/kg). Elimination half-life was 15.40 ± 5.80 h, and clearance was 5876 ± 699 (mL/kg/min). Clinical safety of TAM was determined over a 7-day course of treatment (0.25 mg/kg, orally q 24 h, n = 20). No adverse effects were observed through clinical examination, blood hematology, serum biochemistry, ophthalmological and reproductive examinations. Endometrial edema observed in some mares was attributed to normal cyclic activity.

Conclusions

Tamoxifen has moderate oral bioavailability and a large volume of distribution, with three main metabolites in horses. Additionally, oral TAM administration over a 7-day treatment period demonstrated to be clinically safe, without adverse effects on clinical, hematological or serum biochemical parameters. These data could contribute to the continued research into this drug’s potential for the treatment of different inflammatory conditions in equine species.

Electronic supplementary material

The online version of this article (10.1186/s13620-019-0143-7) contains supplementary material, which is available to authorized users.

Nursing students learning the pharmacology of diabetes mellitus with complexity-based computerized models: A quasi-experimental study

BACKGROUND

Pharmacology is a crucial component of medications administration in nursing, yet nursing students generally find it difficult and self-rate their pharmacology skills as low.

OBJECTIVES

To evaluate nursing students learning pharmacology with the Pharmacology Inter-Leaved Learning-Cells environment, a novel approach to modeling biochemical interactions using a multiscale, computer-based model with a complexity perspective based on a small set of entities and simple rules. This environment represents molecules, organelles and cells to enhance the understanding of cellular processes, and combines these cells at a higher scale to obtain whole-body interactions.

PARTICIPANTS

Sophomore nursing students who learned the pharmacology of diabetes mellitus with the Pharmacology Inter-Leaved Learning-Cells environment (experimental group; n=94) or via a lecture-based curriculum (comparison group; n=54).

METHODS

A quasi-experimental pre- and post-test design was conducted. The Pharmacology-Diabetes-Mellitus questionnaire and the course's final exam were used to evaluate students' knowledge of the pharmacology of diabetes mellitus.

RESULTS

Conceptual learning was significantly higher for the experimental than for the comparison group for the course final exam scores (unpaired t=-3.8, p<0.001) and for the Pharmacology-Diabetes-Mellitus questionnaire (U=942, p<0.001). The largest effect size for the Pharmacology-Diabetes-Mellitus questionnaire was for the medication action subscale. Analysis of complex-systems component reasoning revealed a significant difference for micro-macro transitions between the levels (F(1, 82)=6.9, p<0.05).

CONCLUSIONS

Learning with complexity-based computerized models is highly effective and enhances the understanding of moving between micro and macro levels of the biochemical phenomena, this is then related to better understanding of medication actions. Moreover, the Pharmacology Inter-Leaved Learning-Cells approach provides a more general reasoning scheme for biochemical processes, which enhances pharmacology learning beyond the specific topic learned. The present study implies that deeper understanding of pharmacology will support nursing students' clinical decisions and empower their proficiency in medications administration.

Fundamentals of Population Pharmacokinetic Modelling : Modelling and Software

Population pharmacokinetic modelling is widely used within the field of clinical pharmacology as it helps to define the sources and correlates of pharmacokinetic variability in target patient populations and their impact upon drug disposition. This review focuses on the fundamentals of population pharmacokinetic modelling and provides an overview of the commonly available software programs that perform these functions.This review attempts to define the common, fundamental aspects of population pharmacokinetic modelling through a discussion of the literature describing the techniques and placing them in the appropriate context. An overview of the most commonly available software programs is also provided.Population pharmacokinetic modelling is a powerful approach where sources and correlates of pharmacokinetic variability can be identified in a target patient population receiving a pharmacological agent. There is a need to further standardize and establish the best approaches in modelling so that any model created can be systematically evaluated and the results relied upon. Various nonlinear mixed-effects modelling methods, packaged in a variety of software programs, are available today. When selecting population pharmacokinetic software programs, the consumer needs to consider several factors, including usability (e.g. user interface, native platform, price, input and output specificity, as well as intuitiveness), content (e.g. algorithms and data output) and support (e.g. technical and clinical).

The use of wireless laptop computers for computer-assisted learning in pharmacokinetics

OBJECTIVE

To implement computer-assisted learning workshops into pharmacokinetics courses in a doctor of pharmacy (PharmD) program.

DESIGN

Workshops were designed for students to utilize computer software programs on laptop computers to build pharmacokinetic models to predict drug concentrations resulting from various dosage regimens. In addition, students were able to visualize through graphing programs how altering different parameters changed drug concentration-time curves. Surveys were conducted to measure students' attitudes toward computer technology before and after implementation. Finally, traditional examinations were used to evaluate student learning.

ASSESSMENT

Doctor of pharmacy students responded favorably to the use of wireless laptop computers in problem-based pharmacokinetic workshops. Eighty-eight percent (n = 61/69) and 82% (n = 55/67) of PharmD students completed surveys before and after computer implementation, respectively. Prior to implementation, 95% of students agreed that computers would enhance learning in pharmacokinetics. After implementation, 98% of students strongly agreed (p < 0.05) that computers enhanced learning. Examination results were significantly higher after computer implementation (89% with computers vs. 84% without computers; p = 0.01).

CONCLUSION

Implementation of wireless laptop computers in a pharmacokinetic course enabled students to construct their own pharmacokinetic models that could respond to changing parameters. Students had greater comprehension and were better able to interpret results and provide appropriate recommendations. Computer-assisted pharmacokinetic techniques can be powerful tools when making decisions about drug therapy.

[An example of simulation for a better understanding of PK/PD relationship of antibiotics]

The interpretation of PK/PD indices is specific to each class of antibiotics. In order to illustrate this, we developed a multidisciplinary tutorial program based on simulation of clinical cases. Three drugs were included in this software: tobramycin, vancomycin and azithromycin. From the dosage regimen proposed by the user, the model simulates a plotting of antibiotic plasma concentrations vs. time (tobramycin, vancomycin and azithromycin) and tissue concentrations (azithromycin). Peak and trough concentrations are calculated at steady-state. A commentary is provided to evaluate the efficacy of treatment and to assist the user in improving his prescription of tobramycin or vancomycin. T(> MIC) (time the concentration remains above the MIC) and AUC(24) (area under the concentration-time curve) are calculated in plasma and tissues for azithromycin. In order to create a link between theoretical pharmacokinetics and clinical practice, we propose this model as a simulation of antibiotic monitoring. We put the emphasis on interactivity and simulation, leading to applied reasoning and decision making. It illustrates (i) the influence of pharmacokinetic parameters, location of infection and bactericidal kinetics on the use of three different classes of antibiotics, (ii) the role of route of administration, dosing and intervals between administrations on therapeutic response and (iii) the influence of erratic administrations on clinical efficacy.