Multi-center prospective population pharmacokinetic study and the performance of web-based individual dose optimization application of intravenous vancomycin for adults in Hong Kong: A study protocol.

A recent consensus guideline recommends migrating the therapeutic drug monitoring practice for intravenous vancomycin for the treatment of methicillin-resistant Staphylococcus aureus infection from the traditional trough-based approach to the Bayesian approach based on area under curve to improve clinical outcomes. To support the implementation of the new strategy for hospitals under Hospital Authority, Hong Kong, this study is being proposed to (1) estimate and validate a population pharmacokinetic model of intravenous vancomycin for local adults, (2) develop a web-based individual dose optimization application for clinical use, and (3) evaluate the performance of the application by comparing the treatment outcomes and clinical satisfaction against the traditional approach. 300 adult subjects prescribed with intravenous vancomycin and not on renal replacement therapy will be recruited for population pharmacokinetic model development and validation. Sex, age, body weight, serum creatinine level, intravenous vancomycin dosing records, serum vancomycin concentrations etc. will be collected from several electronic health record systems maintained by Hospital Authority. Parameter estimation will be performed using non-linear mixed-effect modeling techniques. The web-based individual dose optimization application is based on a previously reported application and is built using R and the package shiny. Data from another 50 subjects will be collected during the last three months of the study period and treated as informed by the developed application and compared against historical control for clinical outcomes. Since the study will incur extra blood-taking procedures from patients, informed consent is required. Other than that, recruited subjects should receive medical treatments as usual. Identifiable patient data will be available only to site investigators and clinicians in each hospital. The study protocol and informed consent forms have been approved by the Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee (reference number: NTEC-2021-0215) and registered at the Chinese Clinical Trial Registry (registration number: ChiCTR2100048714).

Using Electronic Health Records for Personalized Dosing of Intravenous Vancomycin in Critically Ill Neonates: Model and Web-Based Interface Development Study.

BACKGROUND: Intravenous (IV) vancomycin is used in the treatment of severe infection in neonates. However, its efficacy is compromised by elevated risks of acute kidney injury. The risk is even higher among neonates admitted to the neonatal intensive care unit (NICU), in whom the pharmacokinetics of vancomycin vary widely. Therapeutic drug monitoring is an integral part of vancomycin treatment to balance efficacy against toxicity. It involves individual dose adjustments based on the observed serum vancomycin concentration (VCs). However, the existing trough-based approach shows poor evidence for clinical benefits. The updated clinical practice guideline recommends population pharmacokinetic (popPK) model-based approaches, targeting area under curve, preferably through the Bayesian approach. Since Bayesian methods cannot be performed manually and require specialized computer programs, there is a need to provide clinicians with a user-friendly interface to facilitate accurate personalized dosing recommendations for vancomycin in critically ill neonates. OBJECTIVE: We used medical data from electronic health records (EHRs) to develop a popPK model and subsequently build a web-based interface to perform model-based individual dose optimization of IV vancomycin for NICU patients in local medical institutions. METHODS: Medical data of subjects prescribed IV vancomycin in the NICUs of Prince of Wales Hospital and Queen Elizabeth Hospital in Hong Kong were extracted from EHRs, namely the Clinical Information System, In-Patient Medication Order Entry, and electronic Patient Record. Patient demographics, such as body weight and postmenstrual age (PMA), serum creatinine (SCr), vancomycin administration records, and VCs were collected. The popPK model employed a 2-compartment infusion model. Various covariate models were tested against body weight, PMA, and SCr, and were evaluated for the best goodness of fit. A previously published web-based dosing interface was adapted to develop the interface in this study. RESULTS: The final data set included EHR data extracted from 207 subjects, with a total of 689 VCs measurements. The final model chosen explained 82% of the variability in vancomycin clearance. All parameter estimates were within the bootstrapping CIs. Predictive plots, residual plots, and visual predictive checks demonstrated good model predictability. Model approximations showed that the model-based Bayesian approach consistently promoted a probability of target attainment (PTA) above 75% for all subjects, while only half of the subjects could achieve a PTA over 50% with the trough-based approach. The dosing interface was developed with the capability to optimize individual doses with the model-based empirical or Bayesian approach. CONCLUSIONS: Using EHRs, a satisfactory popPK model was verified and adopted to develop a web-based individual dose optimization interface. The interface is expected to improve treatment outcomes of IV vancomycin for severe infections among critically ill neonates. This study provides the foundation for a cohort study to demonstrate the utility of the new approach compared with previous dosing methods.

Evaluation of the estimation and classification performance of NONMEM when applying mixture model for drug clearance.

Maximum likelihood estimation of parameters involving mixture model is known to have significant and specific patterns of errors. Population pharmacokinetic (PopPK) modeling using NONMEM is no exception. A few relevant studies on estimation and classification performance were done, but a comprehensive study was not yet available. The current study aims to evaluate performance and likelihood ratio test (LRT)-based true covariate detection rate when fitting a bimodal mixture of drug clearance (CL) in NONMEM. A large number of PopPK datasets with various settings were simulated and then estimated. The estimates were compared to the simulated values and summarized. The separation between the CL distributions of the two subpopulations is systematically overestimated. The major factor associated with the performance is the change in the minimum objective function value after removing the mixture component (dOFV). Other significant factors include estimated disparity index (DI), estimated mixing proportion, and number of subjects in the dataset. Small dOFV and large estimated DI are associated with the worst performance. Omitting a true mixture resulted in reduced true covariate detection rates. It is recommended that on top of routinely generated standard errors and model diagnostics, dOFV, and other factors when necessary, should be taken into account for the evaluation of performance when fitting mixture model using NONMEM. In addition, when fitting mixture model for CL is intended, the mixture component should be introduced prior to LRT-based covariate model development for CL.

Oral delivery of paclitaxel by polymeric micelles: A comparison of different block length on uptake, permeability and oral bioavailability.

Drug solubility and permeability are two major challenges affecting oral delivery, the most popular route of drug administration. Polymeric micelles is an emerging technology for overcoming the current oral drug delivery hurdles. Previous study primarily focused on developing new polymers or new micellar systems and a systematic investigation of the impact of the polymer block length on solubility and permeability enhancement; and their subsequent effect on oral bioavailability is lacking. Herein, by using paclitaxel, a poorly soluble P-glycoproteins (P-gp) substrate, as a model, we aim to assess and compare the drug-loaded micelles prepared with two different molecular weight of poly(ethylene glycol)-block-poly(ε-caprolactone) (PEG-b-PCL), with the ultimate goal of establishing a strong scientific rationale for proper design of formulations for oral drug delivery. PEG-b-PCL (750:570) (PEG17-b-PCL5) and PEG-b-PCL (5k:10k) (PEG114-b-PCL88) effectively enhanced the solubility of paclitaxel compared to the free drug. PEG-b-PCL (750:570) increased both P-gp and non P-gp substrate cellular uptake and increased the apparent permeability coefficient of a P-gp substrate. In vivo animal study showed that PEG-b-PCL micelles efficiently enhanced the oral bioavailability of paclitaxel. In addition to solubility enhancement, polymer choice also plays a pivotal role in determining the oral bioavailability improvement, probably via permeation enhancement. In conclusion, the knowledge gained in this study enables rational design of polymeric micelles to overcome the current challenges of oral drug delivery and it also provides a basis for future clinical translation of the technology.

Population Pharmacokinetic Study and Individual Dose Adjustments of High-Dose Methotrexate in Chinese Pediatric Patients With Acute Lymphoblastic Leukemia or Osteosarcoma.

High-dose methotrexate (>0.5 g/m2 ) is among the first-line chemotherapeutic agents used in treating acute lymphoblastic leukemia (ALL) and osteosarcoma in children. Despite rapid hydration, leucovorin rescue, and routine therapeutic drug monitoring, severe toxicity is not uncommon. This study aimed at developing population pharmacokinetic (popPK) models of high-dose methotrexate for ALL and osteosarcoma and demonstrating the possibility and convenience of popPK model-based individual dose optimization using R and shiny, which is more accessible, efficient, and clinician-friendly than NONMEM. The final data set consists of 36 ALL (354 observations) and 16 osteosarcoma (585 observations) patients. Covariate model building and parameter estimations were done using NONMEM and Perl-speaks-NONMEM. Diagnostic Plots and bootstrapping validated the models' performance and stability. The dose optimizer developed based on the validated models can obtain identical individual parameter estimates as NONMEM. Compared to calling a NONMEM execution and reading its output, estimating individual parameters within R reduces the execution time from 8.7-12.8 seconds to 0.4-1.0 second. For each subject, the dose optimizer can recommend (1) an individualized optimal dose and (2) an individualized range of doses. For osteosarcoma, recommended optimal doses by the optimizer resemble the final doses at which the subjects were eventually stabilized. The dose optimizers developed demonstrated the potential to inform dose adjustments using a model-based, convenient, and efficient tool for high-dose methotrexate. Although the dose optimizer is not meant to replace clinical judgment, it provides the clinician with the individual pharmacokinetics perspective by recommending the (range of) optimal dose.

In silico drug absorption tract: An agent-based biomimetic model for human oral drug absorption.

BACKGROUND: An agent-based modeling approach has been suggested as an alternative to traditional, equation-based modeling methods for describing oral drug absorption. It enables researchers to gain a better understanding of the pharmacokinetic (PK) mechanisms of a drug. This project demonstrates that a biomimetic agent-based model can adequately describe the absorption and disposition kinetics both of midazolam and clonazepam. METHODS: An agent-based biomimetic model, in silico drug absorption tract (ISDAT), was built to mimic oral drug absorption in humans. The model consisted of distinct spaces, membranes, and metabolic enzymes, and it was altogether representative of human physiology relating to oral drug absorption. Simulated experiments were run with the model, and the results were compared to the referent data from clinical equivalence trials. Acceptable similarity was verified by pre-specified criteria, which included 1) qualitative visual matching between the clinical and simulated concentration-time profiles, 2) quantitative similarity indices, namely, weighted root mean squared error (RMSE), and weighted mean absolute percentage error (MAPE) and 3) descriptive similarity which requires less than 25% difference between key PK parameters calculated by the clinical and the simulated concentration-time profiles. The model and its parameters were iteratively refined until all similarity criteria were met. Furthermore, simulated PK experiments were conducted to predict bioavailability (F). For better visualization, a graphical user interface for the model was developed and a video is available in Supporting Information. RESULTS: Simulation results satisfied all three levels of similarity criteria for both drugs. The weighted RMSE was 0.51 and 0.92, and the weighted MAPE was 5.99% and 8.43% for midazolam and clonazepam, respectively. Calculated PK parameter values, including area under the curve (AUC), peak plasma drug concentration (Cmax), time to reach Cmax (Tmax), terminal elimination rate constant (Kel), terminal elimination half life (T1/2), apparent oral clearance (CL/F), and apparent volume of distribution (V/F), were reasonable compared to the referent values. The predicted absolute oral bioavailability (F) was 44% for midazolam (literature reported value, 31-72%) and 93% (literature reported value, ≥ 90%) for clonazepam. CONCLUSION: The ISDAT met all the pre-specified similarity criteria for both midazolam and clonazepam, and demonstrated its ability to describe absorption kinetics of both drugs. Therefore, the validated ISDAT can be a promising platform for further research into the use of similar in silico models for drug absorption kinetics.

Orally-dissolving film for sublingual and buccal delivery of ropinirole.

Ropinirole is a very important treatment option for Parkinson's disease (PD), a major threat to the aging population. However, this drug undergoes extensive first-pass metabolism, resulting in a low oral bioavailability. Moreover, the necessity of frequent administration due to the short half-life of ropinirole may jeopardize patient compliance. Indeed, taking this drug in solid oral dosage forms (e.g. Tablet) can be a challenge because of the tremor, rigidity, limited mobility, and impaired drug absorption experienced by PD patients. In light of these, there is a pressing need to devise formulations for the delivery of ropinirole that allow simple and easy administration and fast drug action, as well as avoidance of first-pass metabolism and overcoming the challenge of impaired absorption due to gastrointestinal dysfunctions, etc. Herein, we seek to overcome all these challenges via sublingual or buccal delivery of orally-dissolving films. Accordingly, we aimed to fabricate and characterize orally-dissolving films of ropinirole and assess their in vivo pharmacokinetics after sublingual and buccal administration. The ropinirole oral film was non-toxic and exhibited fast disintegration and dissolution and was physically stable for at least 28 days. Upon buccal/sublingual administration of the oral films, ropinirole reached the systemic circulation within 15 min and bioavailability was significantly improved, which may be attributable to avoidance of first-pass metabolism via absorption through the oral cavity. In conclusion, our ropinirole oral film improved bioavailability after sublingual or buccal administration. This formulation potentially overcomes biopharmaceutical challenges and provide a convenient means of administration of ropinirole or other anti-PD drugs.

A Review of Food-Drug Interactions on Oral Drug Absorption.

Food effect, also known as food-drug interactions, is a common phenomenon associated with orally administered medications and can be defined as changes in absorption rate or absorption extent. The mechanisms of food effect and their consequences can involve multiple factors, including human post-prandial physiology, properties of the drug, and how the drug is administered. Therefore, it is essential to have a thorough understanding of these mechanisms when recommending whether a specific drug should be taken with or without food. Food-drug interactions can be clinically relevant, especially when they must be avoided to prevent undesirable effects or exploited to optimize medication therapy. This review conducts a literature search that examined studies on food effect. We summarized the literature and identified and discussed common food effect mechanisms. Furthermore, we highlighted drugs that have a clinically significant food effect and discussed the corresponding mechanisms. In addition, this review analyzes the effects of high-fat food or standard meals on the oral drug absorption rate and absorption extent for 229 drugs based on the Biopharmaceutics Drug Disposition Classification System and demonstrates an association between Biopharmaceutics Drug Disposition Classification System class and food effect.

An Agent-Based Approach to Dynamically Represent the Pharmacokinetic Properties of Baicalein.

Baicalein, a typical flavonoid presented in Scutellariae radix, exhibits a unique metabolic profile during first-pass metabolism: parallel glucuronidation and sulfation pathways, with possible substrate inhibition in both pathways. In this project, we aimed to construct an agent-based model to dynamically represent baicalein pharmacokinetics and to verify the substrate inhibition hypothesis. The model consisted of three 3D spaces and two membranes: apical space (S1), intracellular space (S2), basolateral space (S3), apical membrane (M1), and basolateral membrane (M2). In silico enzymes (UDP-glucuronosyltransferases (UGTs) and sulfotransferases (SULTs)) and binder components were placed in S2. The model was then executed to simulate one-pass metabolism experiments of baicalein. With the implementation of a two-site enzyme design, the simulated results captured the preset qualitative and quantitative features of the wet-lab observations. The feasible parameter set showed that substrate inhibition happened in both conjugation pathways of baicalein. The simulation results suggested that the sulfation pathway was dominant at low concentrations and that SULT was more inclined to substrate inhibition than UGT. Cross-model validation was satisfactory. Our findings were consistent with a previously reported catenary model. We conclude that the mechanisms represented by our model are plausible. Our novel modeling approach could dynamically represent the metabolic pathways of baicalein in a Caco-2 system.

Induction of liver cytochrome P450s by Danshen-Gegen formula is the leading cause for its pharmacokinetic interactions with warfarin.

ETHNOPHARMACOLOGICAL RELEVANCE: Although the increased usage of herbal medicine leading to herb-drug interactions is well reported, the mechanism of such interactions between herbal medicines with conventionally prescribed drugs such as warfarin is not yet fully understood. Our previous rat in vivo study demonstrated that co-administration of Danshen-Gegen Formula (DGF), a Radix Salvia miltiorrhiza (Danshen) and Radix Puerariae lobatae (Gegen) containing Chinese medicine formula recently developed for the treatment of cardiovascular disease, with warfarin could cause significant herb-drug interactions. The current study aims to explore the pharmacokinetics-based mechanism of the DGF-warfarin interactions during absorption, distribution and metabolism processes. MATERIALS AND METHODS: Caco-2 cell monolayer model and rat in situ intestinal perfusion model were used to study the DGF-warfarin interactions during the intestinal absorption processes. Male Sprague-Dawley rats were orally administered warfarin in presence and absence of DGF for consecutive 5 days. The microsomal activity and expression of the liver CYP isozymes were determined and compared among different treatment groups. Blood from the rats administered DGF was employed to evaluate effects of DGF on the plasma protein binding of warfarin. RESULTS: Absorption studies demonstrated that DGF could potentially increase the intestinal absorption of warfarin (32% and 75% increase of warfarin Papp in Caco-2 and intestinal perfusion models, respectively) via altering the regional pH environment in GI tract. DGF administration could lead to significant increase in liver microsomal activity and mRNA expression of CYP1A1 and CYP2B1, indicating the potential induction on the liver metabolism of warfarin by DGF. Moreover, it has been proven by ex vivo study that the single-dose administration of DGF could decrease the protein binding of warfarin in plasma by at least 11.6%. CONCLUSION: Collectively, current study demonstrated that DGF could significantly induce the liver phase I metabolism of warfarin, and to a less extent, potentially increase the intestinal absorption and decrease the plasma protein binding of warfarin. The inductive effects of DGF on the liver phase I metabolism of warfarin may be dominantly responsible for the DGF-warfarin pharmacokinetics interactions.

Radix Puerariae: an overview of its chemistry, pharmacology, pharmacokinetics, and clinical use.

Radix Puerariae has been traditionally used for the treatment of diarrhea, acute dysentery, deafness and cardiovascular diseases. Yege (Gegen or Radix Puerariae lobatae), the dried root of Pueraria lobata (Wild.) Ohwi, has been widely used in China and, to a lesser extent, in Japan, Korea, and the United States. Although they have been classified into different categories in Chinese Pharmacopoeia, Yege is often used interchangeably in practice with Fenge (Radix Puerariae thomsonii), which is the dried root of Pueraria thomsonii Benth. Among various commercially available products of Radix Puerariae, injection of puerarin, the major isoflavone from Radix Puerariae, has been most widely used as a vasodilator for the treatment of angina and myocardial infarction. Considering the extensive clinical usage and recent alert of fatal herb-drug interaction of Radix Puerariae, the current review is proposed to cover its traditional applications, pharmacological activities, pharmacokinetics, clinical efficacy, and potential herb-drug interactions aiming to fill in the information gaps of this herb for frontline practitioners. Although various small, poorly designed clinical trials have demonstrated the safety, efficacy, and significant clinical benefits of Radix Puerariae, prospective randomized controlled clinical trials are needed to further establish its effective and safe use.

Relational grounding facilitates development of scientifically useful multiscale models.

We review grounding issues that influence the scientific usefulness of any biomedical multiscale model (MSM). Groundings are the collection of units, dimensions, and/or objects to which a variable or model constituent refers. To date, models that primarily use continuous mathematics rely heavily on absolute grounding, whereas those that primarily use discrete software paradigms (e.g., object-oriented, agent-based, actor) typically employ relational grounding. We review grounding issues and identify strategies to address them. We maintain that grounding issues should be addressed at the start of any MSM project and should be reevaluated throughout the model development process. We make the following points. Grounding decisions influence model flexibility, adaptability, and thus reusability. Grounding choices should be influenced by measures, uncertainty, system information, and the nature of available validation data. Absolute grounding complicates the process of combining models to form larger models unless all are grounded absolutely. Relational grounding facilitates referent knowledge embodiment within computational mechanisms but requires separate model-to-referent mappings. Absolute grounding can simplify integration by forcing common units and, hence, a common integration target, but context change may require model reengineering. Relational grounding enables synthesis of large, composite (multi-module) models that can be robust to context changes. Because biological components have varying degrees of autonomy, corresponding components in MSMs need to do the same. Relational grounding facilitates achieving such autonomy. Biomimetic analogues designed to facilitate translational research and development must have long lifecycles. Exploring mechanisms of normal-to-disease transition requires model components that are grounded relationally. Multi-paradigm modeling requires both hyperspatial and relational grounding.

Mechanistic insight from in silico pharmacokinetic experiments: roles of P-glycoprotein, Cyp3A4 enzymes, and microenvironments.

Saquinavir exhibits paradoxical transport across modified Caco-2 cell monolayers (doi: 10.1124/jpet.103.056390) expressing P-glycoprotein and Cyp3A4. The data implicate complicated intracellular transport mechanisms. Drawing on recent discrete event modeling and simulation advances, we built an in silico analog of the confluent, asymmetric cell monolayer used in the cited work. We call it in silico experimental Caco-2 (cell monolayer) culture (ISECC). Concrete, working, hypothesized spatial mechanisms were implemented. Validation was achieved when in silico experimental results met similarity measure (SM) expectations that targeted 16 wet-lab experimental conditions. Initial mechanistic hypotheses turned out to be necessary parts of a more complicated explanation. We progressed through four stages of an iterative refinement and validation protocol that enabled and facilitated discovery of plausible, new mechanistic details. The process exercised abductive reasoning, a primary means of scientific knowledge creation and creative cognition. The ISECC that survived the most stringent SM challenge produced transport data that was statistically indistinguishable from referent wet-lab observations. It required a 7:1 ratio of apical transporters to metabolizing enzymes, a 97% reduction of efflux activity by an inhibitor, a biased distribution of metabolizing enzymes, heterogeneous intracellular spaces, and restrictions on intracellular drug movement. Experimenting on synthetic analogs such as ISECC provides a former unavailable means of discovering new mechanistic details and testing their plausibility. The approach thus provides a powerful new expansion of the scientific method: an independent, scientific means to challenge, explore, better understand, and improve any inductive mechanism and, importantly, the assumptions on which it rests.

At the biological modeling and simulation frontier.

We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine.

Discovering plausible mechanistic details of hepatic drug interactions.

We sought a single set of mechanisms that could provide a quantitative explanation of three pairs of published time series data: perfusate concentration of digoxin and its metabolite in perfusates of isolated perfused rat livers 1) in the absence of any predose and with a predose of either 2) the uptake inhibitor rifampicin or 3) the efflux inhibitor quinidine. We used the synthetic modeling and simulation method because it provides a means of developing a scientific, experimental approach to unraveling and understanding some of the complexities of drug-drug interactions. We plugged together validated, quasi-autonomous software components to form abstract but mechanistically realistic analogs of livers undergoing perfusion [recirculating in silico livers (RISLs)], into which we could add objects representing each of the above three drugs, alone or in combination. Each RISL was a hypothesis about plausible mechanisms responsible for the referent time series data. Simulations tested each hypothesis. We used similarity measures (SMs) to compare results to the six sets of referent data. From many candidates, we identified an RISL having time-invariant mechanisms that achieved a weak SM (SM-1) but failed to achieve a stronger SM. Replacing four time-invariant with time-variant mechanisms along with addition of new enzyme and transporter components achieved the most stringent SM: simulated digoxin and metabolite perfusate levels were experimentally indistinguishable from the referent data for all three treatments. The mechanisms simulated unanticipated loss of hepatic viability during the original wet-lab experiments: erosion of hepatic accessibility and of enzyme and transporter activities.

Mechanistic simulations explain paradoxical saquinavir metabolism during in vitro vectorial transport study.

We were confronted by an unexpected observation: in a Transwell study of confluent, Cyp3A4 and P-gp-expressing Caco-2 cells, higher intracellular saquinavir levels, yet less first metabolite (M7) formation were observed following apical dosing, compared to basal dosing. To test two seemingly plausible hypothesized explanations, we constructed an in silico working analogue using the synthetic method. Neither mechanism alone was sufficient, but when combined and tuned within the analogue, the results generated were a semi-quantitative match to the experimental data. After 60 cycles, more of the simulated dose was present within analogue cells as parent drug after apical dosing. Furthermore, less M7 was present after apical dose. The paradox disappeared by having simulated drugs equilibrate among separate intracellular zones. Building, studying, and exploring mechanistic explanations for complex wet-lab phenomena using the new methods improved insight into the referent system, while providing a straightforward, scientific means of testing the plausibility of mechanistic hypotheses.

Applying models of targeted drug delivery to gene delivery.

Gene delivery requires targeted delivery systems. Exploratory simulations using models of targeted drug delivery helps one assess the worthiness of such systems, and helps quantify the expected therapeutic benefits of the systems. The drug targeting index (DTI), a ratio of availabilities, is a measure of pharmacokinetic benefit of the delivery device, based on a combination of a physiologically-based pharmacokinetic model and a single pharmacodynamic Emaxmodel. Pharmacodynamic outcomes are quantified by the degree of separation between the dose-response and dose-toxicity curves (SRT). Simulations are undertaken to investigate the potential linkage of DTI and SRT, a pharmacodynamic outcome. A significant positive linear relationship is found between the DTI and SRT. The relationship can be translated into a minimum pharmacokinetic requirement that can be used to guide making decisions regarding whether or not further pursue the development of a candidate gene-delivery device as a therapeutic agent.