Artificial Intelligence and Machine Learning Applied at the Point of Care.

INTRODUCTION: The increasing availability of healthcare data and rapid development of big data analytic methods has opened new avenues for use of Artificial Intelligence (AI)- and Machine Learning (ML)-based technology in medical practice. However, applications at the point of care are still scarce. OBJECTIVE: Review and discuss case studies to understand current capabilities for applying AI/ML in the healthcare setting, and regulatory requirements in the US, Europe and China. METHODS: A targeted narrative literature review of AI/ML based digital tools was performed. Scientific publications (identified in PubMed) and grey literature (identified on the websites of regulatory agencies) were reviewed and analyzed. RESULTS: From the regulatory perspective, AI/ML-based solutions can be considered medical devices (i.e., Software as Medical Device, SaMD). A case series of SaMD is presented. First, tools for monitoring and remote management of chronic diseases are presented. Second, imaging applications for diagnostic support are discussed. Finally, clinical decision support tools to facilitate the choice of treatment and precision dosing are reviewed. While tested and validated algorithms for precision dosing exist, their implementation at the point of care is limited, and their regulatory and commercialization pathway is not clear. Regulatory requirements depend on the level of risk associated with the use of the device in medical practice, and can be classified into administrative (manufacturing and quality control), software-related (design, specification, hazard analysis, architecture, traceability, software risk analysis, cybersecurity, etc.), clinical evidence (including patient perspectives in some cases), non-clinical evidence (dosing validation and biocompatibility/toxicology) and other, such as e.g. benefit-to-risk determination, risk assessment and mitigation. There generally is an alignment between the US and Europe. China additionally requires that the clinical evidence is applicable to the Chinese population and recommends that a third-party central laboratory evaluates the clinical trial results. CONCLUSIONS: The number of promising AI/ML-based technologies is increasing, but few have been implemented widely at the point of care. The need for external validation, implementation logistics, and data exchange and privacy remain the main obstacles.

Exposure-response characterisation of tildrakizumab in chronic plaque psoriasis: Pooled analysis of 3 randomised controlled trials.

AIMS: In this exposure-response analysis, the dosing regimen for tildrakizumab, an antibody for treating moderate-to-severe chronic plaque psoriasis, was determined using data from 3 randomised controlled trials (P05495/NCT01225731: phase 2b, n = 355; reSURFACE 1/NCT01722331: phase 3, n = 772; reSURFACE 2/NCT01729754: phase 3, n = 1090). METHODS: A maximum drug effect (Emax ) logistic-regression exposure-efficacy model was used to describe the week 12 Psoriasis Area and Severity Index (PASI) responses with average concentration of tildrakizumab during weeks 1-12 (Cavg12 ) as exposure metric. The impact of covariates (e.g., body weight, region) was tested. Exposure-safety, longitudinal pharmacokinetic-pharmacodynamic and risk-benefit analyses were also conducted. RESULTS: At week 12, Emax was estimated at 62.2, 37.9 and 14.6% of responders for PASI75/90/100, respectively. Exposure-response curves plateaued at exposures >5 µg mL-1 . Heavier subjects had a lower response rate to placebo as measured by PASI75/90/100 than lighter subjects. PASI100 placebo response was less in subjects with higher baseline PASI score and older age. Simulated week 12 PASI75 increased by ≤4% on increasing the dose from 100 to 200 mg every 12 weeks (Q12W). The pharmacokinetic-pharmacodynamic model adequately described the time course of PASI change after treatment in the entire population and in each subject. Risk-benefit profiles were favourable for the 100- and 200-mg doses in different weight subgroups. CONCLUSIONS: Patients with moderate-to-severe psoriasis should receive 100-mg subcutaneous tildrakizumab Q12W. Patients with high body weight (>90 kg) may benefit from a higher dose (200-mg Q12W).

Maternal-Neonatal Raltegravir Population Pharmacokinetics Modeling: Implications for Initial Neonatal Dosing.

Raltegravir readily crosses the placenta to the fetus with maternal use during pregnancy. After birth, neonatal raltegravir elimination is highly variable and often extremely prolonged, with some neonates demonstrating rising profiles after birth despite removal from the source of extrinsic raltegravir. To establish an appropriate dosing regimen, an integrated maternal-neonatal pharmacokinetics model was built to predict raltegravir plasma concentrations in neonates with in utero raltegravir exposure. Postnatal age and body weight were used as structural covariates. The model predicted rising or decreasing neonatal elimination profiles based on the time of maternal drug administration relative to time of birth and degree of in utero drug disposition into the central and peripheral compartments. Based on this model, it is recommended to delay the first oral dose of raltegravir until 1-2 days of age in those neonates born to mothers who received raltegravir during pregnancy, labor, and delivery.

Operating characteristics of stepwise covariate selection in pharmacometric modeling.

Stepwise covariate modeling (SCM) is a widely used tool in pharmacometric analyses to identify covariates that explain between-subject variability (BSV) in exposure and exposure-response relationships. However, this approach has several potential weaknesses, including over-estimated covariate effect and incorrect selection of covariates due to collinearity. In this work, we investigated the operating characteristics (i.e., accuracy, precision, and power) of SCM in a controlled setting by simulating sixteen scenarios with up to four covariate relationships. The SCM analysis showed a decrease in the power to detect the true covariates as model complexity increased. Furthermore, false highly correlated covariates were frequently selected in place of or in addition to the true covariates. Relative root mean square errors (RMRSE) ranged from 1 to 51% for the fixed effects parameters, increased with the number of covariates included in the model, and were slightly higher than the RMRSE obtained with a simple re-estimation exercise with the true model (i.e., stochastic simulation and estimation). RMRSE for BSV increased with the number of covariates included in the model, with a covariance parameter RMRSE of almost 135% in the most complex scenario. Loose boundary conditions on the continuous covariate power relation appeared to have an impact on the covariate model selection in SCM. A stricter boundary condition helped achieve high power (> 90%), even in the most complex scenario. Finally, reducing the sample size in terms of number of subjects or number of samples proved to have an impact on the power to detect the correct model.

Population-Pharmacokinetic Modeling of Tildrakizumab (MK-3222), an Anti-Interleukin-23-p19 Monoclonal Antibody, in Healthy Volunteers and Subjects with Psoriasis.

BACKGROUND: Tildrakizumab is an anti-interleukin-23p19 monoclonal antibody recently approved for the treatment of chronic plaque psoriasis. METHODS: This analysis characterizes the population pharmacokinetics of subcutaneous tildrakizumab and identifies covariates influencing exposure in 2098 healthy volunteers and subjects with psoriasis. Tested covariates included body weight, formulation type, sex, age, race, serum albumin, creatinine clearance, Japanese origin, prior treatment with a biologic agent, subject status (subjects with psoriasis vs. healthy volunteers), and ethnicity. RESULTS: The pharmacokinetics was described by a one-compartment model with first-order absorption and elimination kinetics, and inter-individual variability on clearance, volume of distribution, and absorption rate constant. The pharmacokinetics was characterized by low clearance and limited volume of distribution. In subjects with psoriasis, the geometric mean clearance (coefficient of variation) was 0.32 L/day (38%), volume of distribution was 10.8 L (24%), and absorption and elimination half-life were 1.5 days (18%) and 23.4 days (23%), respectively, with an absorption lag time of 1.2 h. For the 100-mg dose, steady-state area under the plasma concentration vs. time curve for one dosing interval and maximum plasma concentration were 305 µg*day/mL (41%) and 8.1 µg/mL (34%), respectively. Steady state was achieved by 16 weeks with the clinical regimen (dosing on week 0 and week 4 and every 12 weeks thereafter) with 1.1-fold accumulation in maximum plasma concentration. Healthy subjects had 31% higher bioavailability than subjects with psoriasis. Subjects with increased body weight had a lower area under the plasma concentration-time curve at steady state vs. those with lower body weight. The modeled exposures were contained within clinical comparability bounds for all covariates including body weight. CONCLUSIONS: The pharmacokinetics of tildrakizumab behaves like a typical monoclonal antibody without requiring dosage adjustment. TRIAL REGISTRATION: NCT01729754, NCT01225731, NCT01722331.

Population Pharmacokinetics and Pharmacodynamics of Bezlotoxumab in Adults with Primary and Recurrent Clostridium difficile Infection.

The fully human monoclonal antibody bezlotoxumab is indicated for preventing the recurrence of Clostridioides difficile (formerly Clostridium difficile) infection (CDI) in adults who receive antibacterial treatment for CDI and who are at high risk for a CDI recurrence. The efficacy and safety of 10-mg/kg of body weight bezlotoxumab were demonstrated in two phase 3 trials: the MODIFY I (ClinicalTrials.gov registration number NCT01241552) and MODIFY II (ClinicalTrials.gov registration number NCT01513239) trials. Here, a population pharmacokinetic (popPK) analysis, performed using data from the MODIFY I and II trials (n = 1,515) and from three phase 1 trials (n = 72) to characterize bezlotoxumab pharmacokinetics (PK) in phase 3 clinical trial participants and in healthy subjects, is reported. A stepwise covariate search was conducted to identify factors influencing PK. Post hoc-estimated bezlotoxumab exposures from the popPK model were used to conduct an exposure-response analysis for CDI recurrence. Bezlotoxumab PK were described by a two-compartment model with linear elimination and allometric scaling for clearance and the volume of distribution by body weight. Although the final popPK model included gender, ethnicity (Japanese descent), race (black versus nonblack), and albumin level as significant covariates, the impact of these factors was not clinically meaningful, based on the totality of the PK and clinical experience. Exposure-response analysis of CDI recurrence demonstrated a similar low rate of CDI recurrence over the entire range of exposures achieved in the phase 3 trials, indicating that exposures were on the maximal response plateau of the exposure-response curve. Overall, the analyses confirmed the appropriateness of the 10-mg/kg dose across the phase 3 trial population with no dose adjustments necessary over a broad demographic background.

Pooled population pharmacokinetic model of imipenem in plasma and the lung epithelial lining fluid.

AIMS: Several clinical trials have confirmed the therapeutic benefit of imipenem for treatment of lung infections. There is however no knowledge of the penetration of imipenem into the lung epithelial lining fluid (ELF), the site of action relevant for lung infections. Furthermore, although the plasma pharmacokinetics (PK) of imipenem has been widely studied, most studies have been based on selected patient groups. The aim of this analysis was to characterize imipenem plasma PK across populations and to quantify imipenem ELF penetration. METHODS: A population model for imipenem plasma PK was developed using data obtained from healthy volunteers, elderly subjects and subjects with renal impairment, in order to identify predictors for inter-individual variability (IIV) of imipenem PK. Subsequently, a clinical study which measured plasma and ELF concentrations of imipenem was included in order to quantify lung penetration. RESULTS: A two compartmental model best described the plasma PK of imipenem. Creatinine clearance and body weight were included as subject characteristics predictive for IIV on clearance. Typical estimates for clearance, central and peripheral volume, and inter-compartmental clearance were 11.5 l h(-1) , 9.37 l, 6.41 l, 13.7 l h(-1) , respectively (relative standard error (RSE) <8%). The distribution of imipenem into ELF was described using a time-independent penetration coefficient of 0.44 (RSE 14%). CONCLUSION: The identified lung penetration coefficient confirms the clinical relevance of imipenem for treatment of lung infections, while the population PK model provided insights into predictors of IIV for imipenem PK and may be of relevance to support dose optimization in various subject groups.

Prediction of nomegestrol acetate pharmacokinetics in healthy female adolescents and adults by whole-body physiology-based pharmacokinetic modelling and clinical validation.

OBJECTIVE: Nomegestrol acetate (NOMAC), a selective progestogen, and 17ß-estradiol (E2), which is identical to endogenous oestrogen, are components of a new monophasic combined oral contraceptive--NOMAC/E2. This study aimed to compare pharmacokinetics (PK) of NOMAC in adolescent and adult women following a single dose of NOMAC/E2. STUDY DESIGN: Healthy postmenarcheal adolescent (14-17years) and adult (18-50years) women received a single dose of NOMAC/E2 (2.5mg/1.5mg) in this single-centre, open-label, parallel-group Phase 1 study (EudraCT# 2008-002142-38). Blood samples were obtained for PK analysis, and concentrations of NOMAC, E2 and its metabolite estrone (E1) were determined for up to 129h following dosing to obtain PK data. An independent whole-body physiology-based pharmacokinetic (WB-PBPK) simulation model of NOMAC based on an independent Phase 3 dataset was used to scale NOMAC concentration-time plots to adolescents. RESULTS: Overall, 52 women were screened, of whom 30 (15 adolescents and 15 adults) were enrolled. No statistically significant differences were observed between the adolescent and adult groups for the clinically evaluated NOMAC PK parameters [maximum concentration (Cmax), area under the curve (AUC) and half-life (t1/2)]. The PK of E2 and E1 showed extensive overlap between both age groups. The WB-PBPK model accurately predicted NOMAC AUC and Cmax values in both groups. CONCLUSIONS: No differences were observed in the clinically evaluated PK parameters for NOMAC between adolescent and adult women after a single dose of NOMAC/E2. The WB-PBPK model accurately predicted NOMAC PK data (EudraCT# 2008-002142-38). IMPLICATIONS: PK studies in adolescents are challenging because of ethical considerations. The whole-body physiology-based model described here complements classic noncompartmental and population PK approaches. The utility of this method is its ability to expand to adolescent postmenarcheal girls by using virtual postmenarcheal adolescent population data and applying physiological scaling.

Application of a mechanism-based disease systems model for osteoporosis to clinical data.

A recently proposed mechanism-based disease systems model for osteoporosis (Schmidt et al., J Pharmacokinet Pharmacodyn 38:873-900, 2011) was applied to clinical data from post-menopausal women (n = 767) receiving various doses of the selective estrogen receptor modulator tibolone. Plasma bone-specific alkaline phosphatase activity and urinary N-telopeptide were used as biomarkers reflecting the activity of osteoblasts (bone forming cells) and osteoclasts (bone removing cells), respectively. These data were analyzed in conjunction with data on osteocalcin and on bone mineral density (BMD) (both lumbar spine and total hip), which reflect the activity of both cell types. While the dynamics of bone turnover markers changes rapidly, closely following changes in the activity of bone cells, changes in BMD are slower and have their own dynamics. Application of the mechanism-based disease systems model to the clinical data allowed for an adequate description of the data and yielded parameter estimates that are consistent with physiological values reported in the literature (Lemaire et al., J Theor Biol 229:293-309, 2004). The fitted model enabled characterization of (i) the critical time scales involved in disease progression, (ii) the dynamics of the system during onset and offset of the therapeutic intervention, and (iii) the distinction between responders and low-responders to tibolone treatment.

Translational PK-PD modelling of molecular target modulation for the AMPA receptor positive allosteric modulator Org 26576.

INTRODUCTION: The α-amino-3-hydroxy-5-methylisoxazole-4-propionic acid (AMPA) receptor potentiator Org 26576 represents an interesting pharmacological tool to evaluate the utility of glutamatergic enhancement towards the treatment of psychiatric disorders. In this study, a rat-human translational pharmacokinetic-pharmacodynamic (PK-PD) model of AMPA receptor modulation was used to predict human target engagement and inform dose selection in efficacy clinical trials. METHODS: Modelling and simulation was applied to rat plasma and cerebrospinal fluid (CSF) pharmacokinetic and pharmacodynamic measurements to identify a target concentration (EC(80)) for AMPA receptor modulation. Human plasma pharmacokinetics was determined from 33 healthy volunteers and eight major depressive disorder patients. From four out of these eight patients, CSF PK was also determined. Simulations of human CSF levels were performed for several doses of Org 26576. RESULTS: Org 26576 (0.1-10 mg/kg, i.v.) potentiated rat hippocampal AMPA receptor responses in an exposure-dependant manner. The rat plasma and CSF PK data were fitted by one-compartment model each. The rat CSF PK-PD model yielded an EC(80) value of 593 ng/ml (90% confidence interval 406.8, 1,264.1). The human plasma and CSF PK data were simultaneously well described by a two-compartment model. Simulations showed that in humans at 100 mg QD, CSF levels of Org 26576 would exceed the EC(80) target concentration for about 2 h and that 400 mg BID would engage AMPA receptors for 24 h. CONCLUSION: The modelling approach provided useful insight on the likely human dose-molecular target engagement relationship for Org 26576. Based on the current analysis, 100 and 400 mg BID would be suitable to provide 'phasic' and 'continuous' AMPA receptor engagement, respectively.

Population pharmacokinetic-pharmacodynamic analysis for sugammadex-mediated reversal of rocuronium-induced neuromuscular blockade.

AIMS: An integrated population pharmacokinetic-pharmacodynamic model was developed with the following aims: to simultaneously describe pharmacokinetic behaviour of sugammadex and rocuronium; to establish the pharmacokinetic-pharmacodynamic model for rocuronium-induced neuromuscular blockade and reversal by sugammadex; to evaluate covariate effects; and to explore, by simulation, typical covariate effects on reversal time. METHODS: Data (n= 446) from eight sugammadex clinical studies covering men, women, non-Asians, Asians, paediatrics, adults and the elderly, with various degrees of renal impairment, were used. Modelling and simulation techniques based on physiological principles were applied to capture rocuronium and sugammadex pharmacokinetics and pharmacodynamics and to identify and quantify covariate effects. RESULTS: Sugammadex pharmacokinetics were affected by renal function, bodyweight and race, and rocuronium pharmacokinetics were affected by age, renal function and race. Sevoflurane potentiated rocuronium-induced neuromuscular blockade. Posterior predictive checks and bootstrapping illustrated the accuracy and robustness of the model. External validation showed concordance between observed and predicted reversal times, but interindividual variability in reversal time was pronounced. Simulated reversal times in typical adults were 0.8, 1.5 and 1.4 min upon reversal with sugammadex 16 mg kg(-1) 3 min after rocuronium, sugammadex 4 mg kg(-1) during deep neuromuscular blockade and sugammadex 2 mg kg(-1) during moderate blockade, respectively. Simulations indicated that reversal times were faster in paediatric patients and slightly slower in elderly patients compared with adults. Renal function did not affect reversal time. CONCLUSIONS: Simulations of the therapeutic dosing regimens demonstrated limited impact of age, renal function and sevoflurane use, as predicted reversal time in typical subjects was always <2 min.

Bone physiology, disease and treatment: towards disease system analysis in osteoporosis.

Osteoporosis is a chronic progressive disorder and is regarded as an important worldwide health issue. The development of novel treatments and the comparison of the effects of novel and existing treatments in osteoporosis are complicated by the difficulties of establishing drug effects on disease progression, as reflected in the slowly changing primary biomarker, bone mineral density. In recent years, research has considerably improved our understanding of the pathophysiology of osteoporosis. Specifically, various biomarkers have been identified that reflect bone physiology at the cellular level. These biomarkers mirror the dynamics of bone formation and degradation on a shorter timescale than bone mineral density as a composite measure. These markers can therefore, in principle, be used to characterize the underlying regulatory system and to quantify drug effects in osteoporosis. Recently, the concept of disease system analysis has been proposed as a novel approach to characterize, in a strictly quantitative manner, drug effects on disease progression. This approach integrates physiology, disease progression and drug treatment in a comprehensive mechanism-based model, using dynamic information on a network of biomarkers. This review focuses on the use of disease system analysis for the characterization of drug effects on osteoporosis. It is concluded that, although the development of fully mechanistic disease system models may be practically impossible, parsimonious--but mechanism-based--disease system models may ultimately be used to adequately predict the long-term effects of drug treatment on clinical outcomes.

Assessing the efficiency of mixed effects modelling in quantifying metabolism based drug-drug interactions: using in vitro data as an aid to assess study power.

The clinical assessment of metabolic drug-drug interactions (mDDI) may involve population-based pharmacokinetic (POPPK) assessment as part of Phase 3 clinical trials. The elements of such POPPK study design have not been linked to prior information from in vitro experiments. Using in vitro-in vivo extrapolation techniques, implemented within Simcyp algorithms, the influence of POPPK study design (sample size, concentration-time data points, proportion of the population receiving a concomitant medication (COMED)) was studied in relation to the inhibitory potency of the each COMED. Steady-state concentrations of a candidate compound (compound X; mainly metabolized by cytochrome P450 enzymes, CYP3A4 and CYP2D6) in the presence and absence of COMEDs (including ketoconazole, fluconazole, quinidine and paroxetine as inhibitors) were analysed using non-linear mixed effect modelling (NONMEM). The NONMEM operator was blind to the nature of the COMEDs and the inhibitory effects on model parameters were classified as either statistically (p>0.01 for a change in objective function) or kinetically (COMED effect>2 fold) significant. Using a population study size of 2000, no false-positive cases were identified and, except in one case, no false-negative interaction was observed when >2.5% of patients had received an interacting COMED. The findings increase the confidence in the ability of the mixed effects modelling approach to identify 'true' interactions. However, they also emphasize the importance of study design and the potential value of using pre-clinical information from in vitro studies. Recent US Food and Drug Administration guidance on mDDI has put more emphasis on the use of in vitro systems for detecting and anticipating such effects. Combining these data with the framework of non-linear mixed effect modelling seems a natural progression in the field of assessing mDDI.

Implementation of a transit compartment model for describing drug absorption in pharmacokinetic studies.

PURPOSE: To compare the performance of the standard lag time model (LAG model) with the performance of an analytical solution of the transit compartment model (TRANSIT model) in the evaluation of four pharmacokinetic studies with four different compounds. METHODS: The population pharmacokinetic analyses were performed using NONMEM on concentration-time data of glibenclamide, furosemide, amiloride, and moxonidine. In the TRANSIT model, the optimal number of transit compartments was estimated from the data. This was based on an analytical solution for the change in drug concentration arising from a series of transit compartments with the same first-order transfer rate between each compartment. Goodness-of-fit was assessed by the decrease in objective function value (OFV) and by inspection of diagnostic graphs. RESULTS: With the TRANSIT model, the OFV was significantly lower and the goodness-of-fit was markedly improved in the absorption phase compared with the LAG model for all drugs. The parameter estimates related to the absorption differed between the two models while the estimates of the pharmacokinetic disposition parameters were similar. CONCLUSION: Based on these results, the TRANSIT model is an attractive alternative for modeling drug absorption delay, especially when a LAG model poorly describes the drug absorption phase or is numerically unstable.

Assessment of the relative in vivo potency of the hydroxylated metabolite of darifenacin in its ability to decrease salivary flow using pooled population pharmacokinetic-pharmacodynamic data.

AIMS: To describe the population pharmacokinetic-pharmacodynamic relationship between darifenacin (UK-88,525) and its hydroxylated metabolite (UK-148,993), and the reduction in salivary flow (SF, a M3-mediated response). This enabled an estimation of the in vivo potency of the metabolite to decrease SF relative to that of the parent drug. METHODS: A total of 262 individuals were pooled from 11 Phase 1 studies and one Phase 2 study. A comparison was made between a series of pharmacodynamic models (direct-effect, indirect-effect, link and binding model) using NONMEM. RESULTS: The binding model yielded the best description of the decrease in SF by fully accounting for the time course of the pharmacodynamic effect. An internal validation exercise demonstrated the robustness of this model. Covariate analysis identified a circadian rhythm in SF. This model, with confidence intervals (CI) determined by likelihood profiling, indicated that the relative potency of the metabolite to darifenacin to reduce SF was 11.1% (95% CI 3.8, 19.6). This implied that the metabolite was ninefold less potent than darifenacin in vivo. Accounting for the unbound fraction of darifenacin (2%) and its metabolite (13%), the in vivo protein binding-corrected relative potency was estimated to be 2.1%, indicating that the metabolite was 50-fold less potent than the parent drug. The model supported the assumption that no other metabolites contributing to the impairment of the SF were formed during first-pass, and that the development of sensitization or tolerance was not evident over time. The validation process indicated that the i.v.-oral crossover study was necessary for the estimation of the relative potency. CONCLUSIONS: Population modelling of darifenacin and its hydroxylated metabolite yielded individual pharmacokinetic predictions that could be used to assess the in vivo potency of the metabolite to decrease SF relative to that of the parent drug. The metabolite had a negligible effect on SF.

Population pharmacokinetic modelling of darifenacin and its hydroxylated metabolite using pooled data, incorporating saturable first-pass metabolism, CYP2D6 genotype and formulation-dependent bioavailability.

AIMS: A model describing the population pharmacokinetics of darifenacin and its hydroxylated metabolite was developed from a combined analysis of 18 studies. The relationships between explanatory covariates and pharmacokinetic parameters were explored. METHODS: Plasma concentration data from 337 individuals were pooled from 17 Phase 1 studies (median 28/33 darifenacin/metabolite observations per healthy subject), and one Phase 2 study (median 7/7 darifenacin/metabolite observations per subject) encompassing one intravenous and five different oral formulations (1-45 mg). RESULTS: Non-linear Mixed Effects Models (NONMEM Version VI) described both the population pharmacokinetics of darifenacin and its hydroxylated metabolite with a two-compartment disposition model with first order absorption. The values (mean +/- standard error of the mean) for clearance (CL) and volume of distribution of the central compartment were 40.2 +/- 2.0 l h-1 and 34.7 +/- 4.6 l h-1, respectively, in a typical male CYP2D6 homozygote-extensive metabolizer (Hom-EM). The absolute bioavailability (F) of darifenacin in a Hom-EM after doses of 7.5, 15 or 30 mg extended release formulation (CR) was 15, 19 and 25%, respectively. Factors influencing F were formulation (70-110% higher for CR compared with immediate release following equivalent daily doses), CYP2D6 genotype [heterozygote-extensive metabolizers (Het-EM) and poor metabolizers (PM) experienced 40 and 90%, respectively, higher exposure than Hom-EM irrespective of dose administered] and saturable first-pass metabolism (dose nonlinearity 1.05-1.43-fold). Race affected F, which was 56% lower in Japanese males. The CYP3A4 inhibitors ketoconazole and erythromycin increased F to approximately 100% and ketoconazole decreased CL by 67.5%. CL was 31% lower in females and 10% lower at night. Formulation affected the metabolite absorption/formation rate. Ketoconazole and erythromycin administration resulted in a decrease of 61.2 and 28.8% in exposure to the metabolite, respectively. The covariates race, gender and circadian rhythm accounted for only approximately half of the variability in the estimated exposures to darifenacin. CONCLUSIONS: The pooled analysis provided a descriptive integration of all characteristics and covariates of the pharmacokinetics of darifenacin and its metabolite, enabling interpolation and extrapolation of these key factors.

Bayesian pharmacokinetics of lithium after an acute self-intoxication and subsequent haemodialysis: a case report.

We report a case of a 39-year-old male with bipolar affective disorder who was admitted to hospital with an intentional acute lithium intoxication resulting in renal insufficiency. The patient had previously been treated with lithium, risperidone, fluoxetine and lorazepam, and successfully titrated to lithium levels of 0.7 mmol/l. After overdosing, the lithium level was 5.89 mmol/l and haemodialysis was initiated. A full pharmacokinetic time profile of lithium was obtained. After successful haemodialysis treatment, lithium levels recovered below toxic levels of 1.5 mmol/l in 53 hr. Without intervention non-toxic levels were not expected to have been reached within 6 days, based on computer simulation of predialysis levels. The patient was discharged 6 days after admission without residual symptoms. It was concluded that the lithium intoxication resulted from a combination of lithium overdose and subsequent renal insufficiency due to the overdose. A possible fluoxetine-risperidone interaction was not considered clinically apparent.