Low-dimensional neural ODEs and their application in pharmacokinetics

Machine Learning (ML) is a fast-evolving field, integrated in many of today's scientific disciplines. With the recent development of neural ordinary differential equations (NODEs), ML provides a new tool to model dynamical systems in the field of pharmacology and pharmacometrics, such as pharmacokinetics (PK) or pharmacodynamics. The novel and conceptionally different approach of NODEs compared to classical PK modeling creates challenges but also provides opportunities for its application. In this manuscript, we introduce the functionality of NODEs and develop specific low-dimensional NODE structures based on PK principles. We discuss two challenges of NODEs, overfitting and extrapolation to unseen data, and provide practical solutions to these problems. We illustrate concept and application of our proposed low-dimensional NODE approach with several PK modeling examples, including multi-compartmental, target-mediated drug disposition, and delayed absorption behavior. In all investigated scenarios, the NODEs were able to describe the data well and simulate data for new subjects within the observed dosing range. Finally, we briefly demonstrate how NODEs can be combined with mechanistic models. This research work enhances understanding of how NODEs can be applied in PK analyses and illustrates the potential for NODEs in the field of pharmacology and pharmacometrics.

Prospective study to characterize adalimumab exposure in pediatric patients with rheumatic diseases

BACKGROUND

In pediatric rheumatic diseases (PRD), adalimumab is dosed using fixed weight-based bands irrespective of methotrexate co-treatment, disease activity (DA) or other factors that might influence adalimumab pharmacokinetics (PK). In rheumatoid arthritis (RA) adalimumab exposure between 2-8 mg/L is associated with clinical response. PRD data on adalimumab is scarce. Therefore, this study aimed to analyze adalimumab PK and its variability in PRD treated with/without methotrexate.

METHODS

A two-center prospective study in PRD patients aged 2-18 years treated with adalimumab and methotrexate (GA-M) or adalimumab alone (GA) for ≥ 12 weeks was performed. Adalimumab concentrations were collected 1-9 (maximum concentration; Cmax), and 10-14 days (minimum concentration; Cmin) during ≥ 12 weeks following adalimumab start. Concentrations were analyzed with enzyme-linked immunosorbent assay (lower limit of quantification: 0.5 mg/L). Log-normalized Cmin were compared between GA-M and GA using a standard t-test.

RESULTS

Twenty-eight patients (14 per group), diagnosed with juvenile idiopathic arthritis (71.4%), non-infectious uveitis (25%) or chronic recurrent multifocal osteomyelitis (3.6%) completed the study. GA-M included more females (71.4%; GA 35.7%, p = 0.13). At first study visit, children in GA-M had a slightly longer exposure to adalimumab (17.8 months [IQR 9.6, 21.6]) compared to GA (15.8 months [IQR 8.5, 30.8], p = 0.8). Adalimumab dosing was similar between both groups (median dose 40 mg every 14 days) and observed DA was low. Children in GA-M had a 27% higher median overall exposure compared to GA, although median Cmin adalimumab values were statistically not different (p = 0.3). Cmin values ≥ 8 mg/L (upper limit RA) were more frequently observed in GA-M versus GA (79% versus 64%). Overall, a wide range of Cmin values was observed in PRD (0.5 to 26 mg/L).

CONCLUSION

This study revealed a high heterogeneity in adalimumab exposure in PRD. Adalimumab exposure tended to be higher with methotrexate co-treatment compared to adalimumab monotherapy although differences were not statistically significant. Most children showed adalimumab exposure exceeding those reported for RA with clinical response, particularly with methotrexate co-treatment. This highlights the need of further investigations to establish model-based personalized treatment strategies in PRD to avoid under- and overexposure.

TRIAL REGISTRATION

NCT04042792 , registered 02.08.2019.

Systems pharmacodynamic model of combined gemcitabine and trabectedin in pancreatic cancer cells. Part I.Çô Effects on signal transduction pathways related to tumor growth

Pancreatic ductal adenocarcinoma (PDAC) is often chemotherapy-resistant, and novel drug combinations would fill an unmet clinical need. Previously we reported synergistic cytotoxic effects of gemcitabine and trabectedin on pancreatic cancer cells, but underlying protein-level interaction mechanisms remained unclear. We employed a reliable, sensitive, comprehensive, quantitative, high-throughput IonStar proteomic workflow to investigate the time course of gemcitabine and trabectedin effects, alone and combined, upon pancreatic cancer cells. MiaPaCa-2 cells were incubated with vehicle (controls), gemcitabine, trabectedin, and their combinations over 72 hours. Samples were collected at intervals and analyzed using the label-free IonStar liquid chromatography-mass spectrometry (LC-MS/MS) workflow to provide temporal quantification of protein expression for 4,829 proteins in four experimental groups. To characterize diverse signal transduction pathways, a comprehensive systems pharmacodynamic (SPD) model was developed. The analysis is presented in two parts. Here, Part I describes drug responses in cancer cell growth and migration pathways included in the full model: receptor tyrosine kinase- (RTK), integrin-, G-protein coupled receptor- (GPCR), and calcium-signaling pathways. The developed model revealed multiple underlying mechanisms of drug actions, provides insight into the basis of drug interaction synergism, and offers a scientific rationale for potential drug combination strategies.

Systems Pharmacodynamic Model of Combined Gemcitabine and Trabectedin in Pancreatic Cancer Cells. Part II: Cell Cycle, DNA Damage Response, and Apoptosis Pathways

Despite decades of research efforts, pancreatic adenocarcinoma (PDAC) continues to present a formidable clinical challenge, demanding innovative therapeutic approaches. In a prior study, we reported the synergistic cytotoxic effects of gemcitabine and trabectedin on pancreatic cancer cells. To investigate potential mechanisms underlying this synergistic pharmacodynamic interaction, liquid chromatography-mass spectrometry-based proteomic analysis was performed, and a systems pharmacodynamics model (SPD) was developed to capture pancreatic cancer cell responses to gemcitabine and trabectedin, alone and combined, at the proteome level. Companion report Part I describes the proteomic workflow and drug effects on the upstream portion of the SPD model related to cell growth and migration, specifically the RTK-, integrin-, GPCR-, and calcium-signaling pathways. This report presents Part II of the SPD model. Here we describe drug effects on pathways associated with cell cycle, DNA damage response (DDR), and apoptosis, and provide insights into underlying mechanisms. Drug combination effects on protein changes in the cell cycle- and apoptosis pathways contribute to the synergistic effects observed between gemcitabine and trabectedin. The SPD model was subsequently incorporated into our previously-established cell cycle model, forming a comprehensive, multi-scale quantification platform for evaluating drug effects across multiple scales, spanning the proteomic-, cellular-, and subcellular levels. This approach provides a quantitative mechanistic framework for evaluating drug-drug interactions in combination chemotherapy, and could potentially serve as a tool to predict combinatorial efficacy and assist in target selection.

Somatosensory input in the context of transcranial magnetic stimulation coupled with electroencephalography: An evidence-based overview

The transcranial evoked potential (TEP) is a powerful technique to investigate brain dynamics, but some methodological issues limit its interpretation. A possible contamination of the TEP by electroencephalographic (EEG) responses evoked by the somatosensory input generated by transcranial magnetic stimulation (TMS) has been postulated; nonetheless, a characterization of these responses is lacking. The aim of this work was to review current evidence about possible somatosensory evoked potentials (SEP) induced by sources of somatosensory input in the craniofacial region. Among these, only contraction of craniofacial muscle and stimulation of free cutaneous nerve endings may be able to induce EEG responses, but direct evidence is lacking due to experimental difficulties in isolating these inputs. Notably, EEG evoked activity in this context is represented by a N100/P200 complex, reflecting a saliency-related multimodal response, rather than specific activation of the primary somatosensory cortex. Strategies to minimize or remove these responses by EEG processing still yield uncertain results; therefore, data inspection is of paramount importance to judge a possible contamination of the TEP by multimodal potentials caused by somatosensory input.

Precision pain management in interventional radiology

Pain is a common manifestation of several benign and malignant conditions. Inadequate response to conservative therapies is often succeeded by incremental use of analgesics and opioids; however, such an approach is often ineffective, not well tolerated by patients, and carries the risk of addiction leading to the opioid crisis. Implementing minimally invasive percutaneous procedures, performed by interventional radiologists has proven to be successful in providing safe, effective, and patient-specific therapies across a wide range of painful conditions. In the present narrative review, we will review the repertoire of minimally invasive imaging guided interventions, which have been successfully used to treat common painful benign and malignant conditions. We briefly describe each technique, common indications, and expected results.

Computing optimal drug dosing with OptiDose: implementation in NONMEM

Determining a drug dosing recommendation with a PKPD model can be a laborious and complex task. Recently, an optimal dosing algorithm (OptiDose) was developed to compute the optimal doses for any pharmacometrics/PKPD model for a given dosing scenario. In the present work, we reformulate the underlying optimal control problem and elaborate how to solve it with standard commands in the software NONMEM. To demonstrate the potential of the OptiDose implementation in NONMEM, four relevant but substantially different optimal dosing tasks are solved. In addition, the impact of different dosing scenarios as well as the choice of the therapeutic goal on the computed optimal doses are discussed.

Clinically practical pharmacometrics computer model to evaluate and personalize pharmacotherapy in pediatric rare diseases: application to Graves' disease

Objectives

Graves' disease (GD) with onset in childhood or adolescence is a rare disease (ORPHA:525731). Current pharmacotherapeutic approaches use antithyroid drugs, such as carbimazole, as monotherapy or in combination with thyroxine hormone substitutes, such as levothyroxine, as block-and-replace therapy to normalize thyroid function and improve patients' quality of life. However, in the context of fluctuating disease activity, especially during puberty, a considerable proportion of pediatric patients with GD is suffering from thyroid hormone concentrations outside the therapeutic reference ranges. Our main goal was to develop a clinically practical pharmacometrics computer model that characterizes and predicts individual disease activity in children with various severity of GD under pharmacotherapy.

Methods

Retrospectively collected clinical data from children and adolescents with GD under up to two years of treatment at four different pediatric hospitals in Switzerland were analyzed. Development of the pharmacometrics computer model is based on the non-linear mixed effects approach accounting for inter-individual variability and incorporating individual patient characteristics. Disease severity groups were defined based on free thyroxine (FT4) measurements at diagnosis.

Results

Data from 44 children with GD (75% female, median age 11 years, 62% receiving monotherapy) were analyzed. FT4 measurements were collected in 13, 15, and 16 pediatric patients with mild, moderate, or severe GD, with a median FT4 at diagnosis of 59.9 pmol/l (IQR 48.4, 76.8), and a total of 494 FT4 measurements during a median follow-up of 1.89 years (IQR 1.69, 1.97). We observed no notable difference between severity groups in terms of patient characteristics, daily carbimazole starting doses, and patient years. The final pharmacometrics computer model was developed based on FT4 measurements and on carbimazole or on carbimazole and levothyroxine doses involving two clinically relevant covariate effects: age at diagnosis and disease severity.

Discussion

We present a tailored pharmacometrics computer model that is able to describe individual FT4 dynamics under both, carbimazole monotherapy and carbimazole/levothyroxine block-and-replace therapy accounting for inter-individual disease progression and treatment response in children and adolescents with GD. Such clinically practical and predictive computer model has the potential to facilitate and enhance personalized pharmacotherapy in pediatric GD, reducing over- and underdosing and avoiding negative short- and long-term consequences. Prospective randomized validation trials are warranted to further validate and fine-tune computer-supported personalized dosing in pediatric GD and other rare pediatric diseases.

Evaluation of machine learning methods for covariate data imputation in pharmacometrics

Missing data create challenges in clinical research because they lead to loss of statistical power and potentially to biased results. Missing covariate data must be handled with suitable approaches to prepare datasets for pharmacometric analyses, such as population pharmacokinetic and pharmacodynamic analyses. To this end, various statistical methods have been widely adopted. Here, we introduce two machine-learning (ML) methods capable of imputing missing covariate data in a pharmacometric setting. Based on a previously published pharmacometric analysis, we simulated multiple missing data scenarios. We compared the performance of four established statistical methods, listwise deletion, mean imputation, standard multiple imputation (hereafter "Norm"), and predictive mean matching (PMM) and two ML based methods, random forest (RF) and artificial neural networks (ANNs), to handle missing covariate data in a statistically plausible manner. The investigated ML-based methods can be used to impute missing covariate data in a pharmacometric setting. Both traditional imputation approaches and ML-based methods perform well in the scenarios studied, with some restrictions for individual methods. The three methods exhibiting the best performance in terms of least bias for the investigated scenarios are the statistical method PMM and the two ML-based methods RF and ANN. ML-based approaches had comparable good results to the best performing established method PMM. Furthermore, ML methods provide added flexibility when encountering more complex nonlinear relationships, especially when associated parameters are suitably tuned to enhance predictive performance.

Leveraging Predictive Pharmacometrics-Based Algorithms to Enhance Perinatal Care—Application to Neonatal Jaundice

The field of medicine is undergoing a fundamental change, transforming towards a modern data-driven patient-oriented approach. This paradigm shift also affects perinatal medicine as predictive algorithms and artificial intelligence are applied to enhance and individualize maternal, neonatal and perinatal care. Here, we introduce a pharmacometrics-based mathematical-statistical computer program (PMX-based algorithm) focusing on hyperbilirubinemia, a medical condition affecting half of all newborns. Independent datasets from two different centers consisting of total serum bilirubin measurements were utilized for model development (342 neonates, 1,478 bilirubin measurements) and validation (1,101 neonates, 3,081 bilirubin measurements), respectively. The mathematical-statistical structure of the PMX-based algorithm is a differential equation in the context of non-linear mixed effects modeling, together with Empirical Bayesian Estimation to predict bilirubin kinetics for a new patient. Several clinically relevant prediction scenarios were validated, i.e., prediction up to 24 h based on one bilirubin measurement, and prediction up to 48 h based on two bilirubin measurements. The PMX-based algorithm can be applied in two different clinical scenarios. First, bilirubin kinetics can be predicted up to 24 h based on one single bilirubin measurement with a median relative (absolute) prediction difference of 8.5% (median absolute prediction difference 17.4 μmol/l), and sensitivity and specificity of 95.7 and 96.3%, respectively. Second, bilirubin kinetics can be predicted up to 48 h based on two bilirubin measurements with a median relative (absolute) prediction difference of 9.2% (median absolute prediction difference 21.5 μmol/l), and sensitivity and specificity of 93.0 and 92.1%, respectively. In contrast to currently available nomogram-based static bilirubin stratification, the PMX-based algorithm presented here is a dynamic approach predicting individual bilirubin kinetics up to 48 h, an intelligent, predictive algorithm that can be incorporated in a clinical decision support tool. Such clinical decision support tools have the potential to benefit perinatal medicine facilitating personalized care of mothers and their born and unborn infants.

Modeling of levothyroxine in newborns and infants with congenital hypothyroidism: challenges and opportunities of a rare disease multi-center study

Modeling of retrospectively collected multi-center data of a rare disease in pediatrics is challenging because laboratory data can stem from several decades measured with different assays. Here we present a retrospective pharmacometrics (PMX) based data analysis of the rare disease congenital hypothyroidism (CH) in newborns and infants. Our overall aim is to develop a model that can be applied to optimize dosing in this pediatric patient population since suboptimal treatment of CH during the first 2 years of life is associated with a reduced intelligence quotient between 10 and 14 years. The first goal is to describe a retrospectively collected dataset consisting of 61 newborns and infants with CH up to 2 years of age. Overall, 505 measurements of free thyroxine (FT4) and 510 measurements of thyrotropin or thyroid-stimulating hormone were available from patients receiving substitution treatment with levothyroxine (LT4). The second goal is to introduce a scale/location-scale normalization method to merge available FT4 measurements since 34 different postnatal age- and assay-specific laboratory reference ranges were applied. This method takes into account the change of the distribution of FT4 values over time, i.e. a transformation from right-skewed towards normality during LT4 treatment. The third goal is to develop a practical and useful PMX model for LT4 treatment to characterize FT4 measurements, which is applicable within a clinical setting. In summary, a time-dependent normalization method and a practical PMX model are presented. Since there is no on-going or planned development of new pharmacological approaches for CH, PMX based modeling and simulation can be leveraged to personalize dosing with the goal to enhance longer-term neurological outcome in children with the rare disease CH.

PMMA Bone Cement in Interventional Oncology

Polymethylmethacrylate (PMMA) bone cement is increasingly being used for percutaneous minimally invasive treatments of patients suffering from bone malignancies. PMMA is composed of a polymeric powder and a monomeric liquid. Once mixed, the polymerization process begins and leads to a viscous fluid that can be injected through a bone trocar. Cement progressively hardens within the bone, leading to a viscoelastic solid material. PMMA interacts with the surrounding cancellous bone through mechanical interlocking via interdigitations in trabecular bone. It can also bond with hardware, such as titanium screws, as it penetrates the macro- and micro-irregularities of the hardware. PMMA itself has no antineoplastic effects but may be used as a stand-alone treatment to provide pain palliation and bone consolidation through mechanical reinforcement, notably in areas with high compression load. It can also be used to reinforce the anchorage of screws in case of a landing zone with poor bone quality due to underlying malignant osteolysis.

Delay differential equations based models in NONMEM

Delay differential equations (DDEs) are commonly used in pharmacometric models to describe delays present in pharmacokinetic and pharmacodynamic data analysis. Several DDE solvers have been implemented in NONMEM 7.5 for the first time. Two of them are based on algorithms already applied elsewhere, while others are extensions of existing ordinary differential equations (ODEs) solvers. The purpose of this tutorial is to introduce basic concepts underlying DDE based models and to show how they can be developed using NONMEM. The examples include previously published DDE models such as logistic growth, tumor growth inhibition, indirect response with precursor pool, rheumatoid arthritis, and erythropoiesis-stimulating agents. We evaluated the accuracy of NONMEM DDE solvers, their ability to handle stiff problems, and their performance in parameter estimation using both first-order conditional estimation (FOCE) and the expectation-maximization (EM) method. NONMEM control streams and excerpts from datasets are provided for all discussed examples. All DDE solvers provide accurate and precise solutions with the number of significant digits controlled by the error tolerance parameters. For estimation of population parameters, the EM method is more stable than FOCE regardless of the DDE solver.

Pain relief and local tumour control following percutaneous image-guided cryoablation for spine metastasis: a 12-year single-centre experience

AIM

To assess pain relief and local tumour control retrospectively in spinal metastases undergoing cryoablation.

MATERIALS AND METHODS

Between May 2008 and September 2020, 46 metastases in 41 consecutive patients (mean age 59.7±4.4 [SD] years; range 27-84) were treated with cryoablation in 42 interventional sessions. Patient demographics, procedural data, complications, pain, and local tumour control were analysed retrospectively.

RESULTS

Thirty-one patients (36 spine metastases; 32 sessions) were treated for pain relief and 10 (10 metastases; 10 sessions) for local tumour control. Clinical success was reached in 30/32 (93.8%) interventional palliative sessions. Mean pre-procedural numerical pain rate scale was 6.2±1.7 (SD), and dropped significantly to 3.5±1.8 (SD), 1.9±1.7 (SD), and 1.9±1.8 (SD) at 24-h, 1-month and at the last available follow-up (median 16.5±23.2 [SD] months), respectively. For patients requiring local tumour control, primary clinical success was reached in 6/10 (60%) spinal metastases at median 25-months follow-up. The overall complication rate was 8%, with no secondary fractures or iatrogenic thermal-mediated nerve injuries reported.

CONCLUSION

Percutaneous image-guided cryoablation of spinal metastases is safe and effective in achieving pain relief and local tumour control.

Robot-Assisted Bone Cement Injection

In this article, assistance to bone cement injection is studied, with a focus on vertebroplasty, a procedure dedicated to the treatment of vertebral compression fractures. A robotic system that can remotely be operated at pressures up to 140 bar is presented. It improves cement polymerization control, combining a cold passive exchanger that slows down the cement curing in the syringe and an active exchanger that controls the injected cement temperature. The cement remote injection uses a rate control teleoperation strategy with force feedback to help monitoring the cement state. In addition to laboratory assessments, cadaver experiments were performed to illustrate the satisfactory operation of the whole system.

Vaginal Delivery and Low Gestational Age are Key Risk Factors for Hypernatremia in Neonates<32 Weeks

Background Preterm neonates frequently experience hypernatremia (plasma sodium concentrations >145 mmol/l), which is associated with clinical complications, such as intraventricular hemorrhage.

Study design In this single center retrospective observational study, the following 7 risk factors for hypernatremia were analyzed in very low gestational age (VLGA, below 32 weeks) neonates: gestational age (GA), delivery mode (DM; vaginal or caesarian section), sex, birth weight, small for GA, multiple birth, and antenatal corticosteroids. Machine learning (ML) approaches were applied to obtain probabilities for hypernatremia.

Results 824 VLGA neonates were included (median GA 29.4 weeks, median birth weight 1170 g, caesarean section 83%). 38% of neonates experienced hypernatremia. Maximal sodium concentration of 144 mmol/l (interquartile range 142–147) was observed 52 hours (41–65) after birth. ML identified vaginal delivery and GA as key risk factors for hypernatremia. The risk of hypernatremia increased with lower GA from 22% for GA ≥ 31–32 weeks to 46% for GA < 31 weeks and 60% for GA < 27 weeks. A linear relationship between maximal sodium concentrations and GA was found, showing decreases of 0.29 mmol/l per increasing week GA in neonates with vaginal delivery and 0.49 mmol/l/week after cesarean section. Sex, multiple birth and antenatal corticosteroids were not associated hypernatremia.

Conclusion VLGA neonates with vaginal delivery and low GA have the highest risk for hypernatremia. Early identification of neonates at risk and early intervention may prevent extreme sodium excursions and associated clinical complications.

OptiDose: Computing the Individualized Optimal Drug Dosing Regimen Using Optimal Control

Providing the optimal dosing strategy of a drug for an individual patient is an important task in pharmaceutical sciences and daily clinical application. We developed and validated an optimal dosing algorithm (OptiDose) that computes the optimal individualized dosing regimen for pharmacokinetic-pharmacodynamic models in substantially different scenarios with various routes of administration by solving an optimal control problem. The aim is to compute a control that brings the underlying system as closely as possible to a desired reference function by minimizing a cost functional. In pharmacokinetic-pharmacodynamic modeling, the controls are the administered doses and the reference function can be the disease progression. Drug administration at certain time points provides a finite number of discrete controls, the drug doses, determining the drug concentration and its effect on the disease progression. Consequently, rewriting the cost functional gives a finite-dimensional optimal control problem depending only on the doses. Adjoint techniques allow to compute the gradient of the cost functional efficiently. This admits to solve the optimal control problem with robust algorithms such as quasi-Newton methods from finite-dimensional optimization. OptiDose is applied to three relevant but substantially different pharmacokinetic-pharmacodynamic examples.

The rhythm of a preterm neonate's life: ultradian oscillations of heart rate, body temperature and sleep cycles

The objectives are to characterize oscillations of physiological functions such as heart rate and body temperature, as well as the sleep cycle from behavioral states in generally stable preterm neonates during the first 5 days of life. Heart rate, body temperature as well as behavioral states were collected during a daily 3-h observation interval in 65 preterm neonates within the first 5 days of life. Participants were born before 32 weeks of gestational age or had a birth weight below 1500 g; neonates with asphyxia, proven sepsis or malformation were excluded. In total 263 observation intervals were available. Heart rate and body temperature were analyzed with mathematical models in the context of non-linear mixed effects modeling, and the sleep cycles were characterized with signal processing methods. The average period length of an oscillation in this preterm neonate population was 159 min for heart rate, 290 min for body temperature, and the average sleep cycle duration was 19 min. Oscillation of physiological functions as well as sleep cycles can be characterized in very preterm neonates within the first few days of life. The observed parameters heart rate, body temperature and sleep are running in a seemingly uncorrelated pace at that stage of development. Knowledge about such oscillations may help to guide nursing and medical care in these neonates as they do not yet follow a circadian rhythm.

Copeptin Kinetics and Its Relationship to Osmolality During Rehydration for Diabetic Ketoacidosis in Children

CONTEXT

Copeptin is a surrogate marker for arginine vasopressin (AVP) release in response to hyperosmolal stimuli such as diabetic ketoacidosis (DKA).

OBJECTIVE

The objective of this work is to characterize kinetics of copeptin and osmolality, and their dynamic relationship during rehydration and insulin therapy in children with type 1 diabetes (T1D) and DKA.

DESIGN AND SETTING

A prospective, observational, multicenter study was conducted.

PATIENTS AND INTERVENTION

Children with T1D admitted for DKA underwent serial serum copeptin and osmolality measurements from start of rehydration at 14 time points during 72 hours.

MAIN OUTCOME MEASURES

Measurements included temporal course of copeptin and osmolality (kinetics), relationship between both (dynamics), and association between-subject variability (BSV) (coefficient of variation, CV%).

RESULTS

Twenty-eight children (20 newly diagnosed T1D) aged 1 to 16 years were included. Copeptin decreased from 95 pmol/L (95% CI, 55-136 pmol/L) (CV%, 158%) to 9.7 pmol/L (95% CI, 8.1-11.4 pmol/L) (CV%, 31%) with a 50% recovery time (t1/2) of 7.1 hours (range, 5.1-11.5 hours) (114%). Serum osmolality decreased from 321 mOsm/kg (range, 315-327 mOsm/kg) (4%) to 294 mOsm/kg (range, 292-296 mOsm/kg) (1%) with a t1/2 of 4.3 hours (range, 3.0-5.6 hours) (64%). Copeptin levels doubled with each osmolality increase by 15 mOsm/kg (range, 10-21 mOsm/kg) (59%), from 9.8 pmol/L (range, 7.3-12.3 pmol/L) (48%) to 280 mOsm/kg. Copeptin kinetics differed between newly diagnosed and known T1D patients (P = .001), and less between mild vs moderate-severe DKA (P = .04).

CONCLUSIONS

First, this study characterized for the first time copeptin kinetics and dynamics in the high hyperosmolar range in children with DKA. Second, it revealed significant differences in copeptin kinetics between newly diagnosed and known T1D patients that may be explained by changes at the osmoreceptor and renal AVP receptor level due to longstanding osmotic diuresis and DKA.

Impact of old and very old age on outcomes in patients with acute ischemic stroke undergoing endovascular therapy

Background and purpose

Only a restricted number of elderly patients has been included in the majority of recent endovascular stroke trials. We provided an analysis about differences in outcomes after mechanical thrombectomy (MT) according to age.

Methods

A retrospective analysis of an observational registry of patients with anterior large vessel acute ischemic stroke was performed. Main analysis was performed comparing patients &lt;80 vs. ≥80 years old. A sensitivity analysis was performed comparing 3 age groups: i) &lt;80 years; ii) 80–84 years; iii) ≥85 years. Outcomes were: i) any hemorrhagic infarction; ii) alive with disability; iii) death; iv) a composite outcome of alive with disability/death.

Results

615 patients were identified. 227 (36.9%) patients were ≥80 years old, with 115 (18.5%) ≥85 years old. Elderly (≥80 years) patients showed a higher modified Rankin Scale (mRS) at discharge and 3-months follow-up (F=9.819, p=0.001) [Figure 1]. Comparing the three groups (&lt;80 years, 80–84 years, ≥85 years) a progressively higher mRS was found at discharge and 3 months follow-up (F=4.899, p=0.008). A progressively higher rate of death and composite outcome between the age groups was found, both in the main and sensitivity analyses. In the logistic regression analysis age ≥80 years was found associated with an increased risk of death (odds ratio [OR]: 2.25, 95% confidence interval [CI]: 1.27–4.00) and showed a trend in higher risk for composite outcome (OR: 1.61, 95% CI: 0.92–2.281). No difference was found between 80–84 years and &lt;80 years patients, while very elderly (≥85 years) had an increased risk of death (OR: 2.85, 95% CI: 1.60–5.10) and composite outcome (OR: 2.37, 95% CI: 1.30–4.33).

Conclusions

In our analysis elderly patients have an increased risk of death and composite outcome of disability and death. In particular, this risk appears to be significantly higher in very elderly patients (≥85 years old).

Figure 1. mRS according to main analysis

Funding Acknowledgement

Type of funding source: None

Pharmacokinetics and Pharmacodynamics Models of Tumor Growth and Anticancer Effects in Discrete Time

We study the h-discrete and h-discrete fractional representation of a pharmacokinetics-pharmacodynamics (PK-PD) model describing tumor growth and anticancer effects in continuous time considering a time scale h𝕅0, where h > 0. Since the measurements of the drug concentration in plasma were taken hourly, we consider h = 1/24 and obtain the model in discrete time (i.e. hourly). We then continue with fractionalizing the h-discrete nabla operator in the h-discrete model to obtain the model as a system of nabla h-fractional difference equations. In order to solve the fractional h-discrete system analytically we state and prove some theorems in the theory of discrete fractional calculus. After estimating and getting confidence intervals of the model parameters, we compare residual squared sum values of the models in one table. Our study shows that the new introduced models provide fitting as good as the existing models in continuous time.

Attitudes to dental visits in a child population attending public dental health care in Jönköping, Sweden-trends over 40 years

PURPOSE

To study attitudes to dental visits in a child population attending public dental health care between 1973 and 2013.

METHODS

Random samples of individuals aged 3, 5, 10, 15 and 20 years took part in cross-sectional studies based on clinical parameters and a questionnaire, all repeated every 10 years using the same study design. One question related to dental attitude; "What do you feel at the prospect of an appointment with a dentist?". The affirmative answers were full of expectation, unaffected, ill at ease, frightened and sick, representing positive, neutral and negative attitudes. During the same 40-year period, a dental health care system based on prevention and regular dental appointments was reinforced.

RESULTS

In preschool children, the most pronounced change was an increase in the number of children reporting full of expectation during the study period. In 10-, 15- and 20-year-olds, there was an increase in unaffected and a decrease in ill at ease over time. A reduction in frightened was only seen in the 20-year-olds.

CONCLUSIONS

Despite a simultaneous dramatic decline in caries and a reduced need for operative dental treatment, the dental attitudes were fairly constant during the 40-year period. The moderate shifts to more positive or neutral dental attitudes were merely seen during the first 10 or 20 years. After this, the changes in answers were limited. This leads to the assumption that factors other than dental treatment experiences have an impact on dental attitudes in children.

Efficacy of Parent-Infant-Psychotherapy with mothers with postpartum mental disorder: study protocol of the randomized controlled trial as part of the SKKIPPI project

BACKGROUND

After the birth of a child, many mothers and fathers experience postpartum mental disorders like depression, anxiety, obsessive-compulsive disorder, stress or other illnesses. This endangers the establishment of a secure attachment between the children and their primary caregivers. Early problems in parent-child interaction can have adverse long-term effects on the family and the child's well-being. In order to prevent a transgenerational transmission of mental disorders, it is necessary to evaluate psychotherapeutic interventions that target psychologically burdened parents of infants or toddlers. The aim of this trial is to investigate the efficacy of Parent-Infant-Psychotherapy (PIP) for mothers with postpartum mental disorder and their infants (0-12 months).

METHODS/DESIGN

In this open, randomized controlled intervention trial 180 mother-infant-dyads will be included and randomly allocated to 12 sessions of PIP or care as usual. The interventions take place either in inpatient adult psychiatric departments or in outpatient settings with home visits. The primary outcome is the change in maternal sensitivity assessed by the Sensitivity subscale of the Emotional Availability Scale (EAS) through videotaped dyadic play-interactions after 6 weeks. Secondary outcomes are maternal psychopathology, stress, parental reflective functioning, infant development and attachment after 6 weeks and 12 months. In addition, maternal attachment (AAI) and reflective functioning (AAI) will be analyzed as potential moderators, and resource usage in the German health system as well as associated costs will be evaluated.

DISCUSSION

There is increasing demand for well-controlled studies on psychotherapeutic interventions in the postpartum period that do not only focus on particular risk groups. This randomized controlled trial (RCT) represents one of the first studies to investigate the efficacy of PIP in inpatient psychiatric departments and outpatient care centers in Germany. The results will fill knowledge gaps on the factors contributing to symptom reduction in postpartum mental disorders and improvements in mother-child relationships and help in developing preventive and therapeutic strategies for the fragmented German health care system.

TRIAL REGISTRATION

German Register for Clinical Trials, ID: DRKS00016353.

Caffeine preserves quiet sleep in preterm neonates

Caffeine is widely used in preterm neonates suffering from apnea of prematurity (AOP), and it has become one of the most frequently prescribed medications in neonatal intensive care units. Goal of this study is to investigate how caffeine citrate treatment affects sleep-wake behavior in preterm neonates. The observational study consists of 64 preterm neonates during their first 5 days of life with gestational age (GA) <32 weeks or very low birthweight of < 1500 g. A total of 52 patients treated with caffeine citrate and 12 patients without caffeine citrate were included. Sleep-wake behavior was scored in three stages: active sleep, quiet sleep, and wakefulness. Individual caffeine concentration of every neonate was simulated with a pharmacokinetic model. In neonates with GA ≥ 28 weeks, wakefulness increased and active sleep decreased with increasing caffeine concentrations, whereas quiet sleep remained unchanged. In neonates with GA < 28 weeks, no clear caffeine effects on sleep-wake behavior could be demonstrated. Caffeine increases fraction of wakefulness, alertness, and most probably also arousability at cost of active but not quiet sleep in preterm neonates. As such, caffeine should therefore not affect time for physical and cerebral regeneration during sleep in preterm neonates.

MON-081 Mathematical Modeling of Residual Endogenous FT4 Synthesis and Exogenous L-Thyroxine Administration over the First 2 Years of Life in Infants with Congenital Hypothyroidism

L-Thyroxine (L-T4) is the treatment of choice of congenital hypothyroidism (CH). Longitudinal measurements of free T4 (FT4) serum concentrations were collected over the first two years of life with oral L-T4 treatment in infants with CH. Purpose of this study was to develop an integrated mathematical model to characterize the kinetics of exogenous L-Thyroxine (L-T4) after multiple dosing in infants with CH, and the dynamics of residual endogenous FT4 synthesis under treatment in the context of severe, moderate and mild disease.

A total of 200 FT4 concentrations from 30 patients were available for analysis. At start of treatment, mean (standard deviation [SD]) postnatal age and weight of the population were 11 (8) days and 3.9 (1.3) kg. Mean (SD) pretreatment FT4 concentration was 11.3 (7.4) pmol/L. Measured FT4 concentrations were modelled as sum of residual endogenous FT4 and exogenously administered FT4 (L-T4). The integrated mathematical model consists of an absorption compartment for the exogenous FT4 administration, and a central compartment for measured FT4 with linear elimination. Hence, for residual endogenous and exogenous FT4 the same elimination rate constant was assumed. For the residual endogenous synthesis, different approaches were tested: a constant production and typical time-dependent production functions. FT4 data were analyzed using nonlinear mixed-effects modeling.

The integrated mathematical model with a time-dependent non-linear Emax function describing a decreasing residual endogenous FT4 synthesis for increasing time provided the best data fit in terms of Akaike value and various goodness-of-fit plots. This is in line with the expected progressive suppression of the thyroid stimulating hormone by the exogenous FT4, and the subsequent decrease of residual FT4 endogenous synthesis. The developed mathematical model allows simulation of FT4 pharmacokinetic profiles for different disease severities and different dosing regimens.

Pharmacometrics and Machine Learning Partner to Advance Clinical Data Analysis

Clinical pharmacology is a multidisciplinary data sciences field that utilizes mathematical and statistical methods to generate maximal knowledge from data. Pharmacometrics (PMX) is a well-recognized tool to characterize disease progression, pharmacokinetics, and risk factors. Because the amount of data produced keeps growing with increasing pace, the computational effort necessary for PMX models is also increasing. Additionally, computationally efficient methods, such as machine learning (ML) are becoming increasingly important in medicine. However, ML is currently not an integrated part of PMX, for various reasons. The goals of this article are to (i) provide an introduction to ML classification methods, (ii) provide examples for a ML classification analysis to identify covariates based on specific research questions, (iii) examine a clinically relevant example to investigate possible relationships of ML and PMX, and (iv) present a summary of ML and PMX tasks to develop clinical decision support tools.

4329Mega-studies in heart failure, effect dilution in examination of new therapies

Aims

All phase 3 studies in patients with acute heart failure (AHF) and HF with preserved ejection fraction (HFpEF) have failed in the last decades. We explore the likelihood that the negative results are due to chance and/or to study size and dilution of statistical power.

Methods and results

First, using simulations, we examined the probability that a positive finding in phase 2 would result in studying truly effective drugs in phase 3. We simulated phase 2 studies under six scenarios where the range of true relative risk (RR) for an outcome of interest varied from 0.5 (major benefit) to 1.15 (some harm). The proportion of simulated studies where the RR <0.8 (we assumed that a 20% or greater risk reduction reflects an effective drug) ranged from 6% to 42% across the six scenarios studied. To further simulate “real life” clinical research, we simulated a continuous surrogate outcome that was linearly related to the true RR in each simulation of each scenario. Regardless of criteria considered for a positive phase 2 trial, results suggest that even in our worst-case scenario, where overall only 6% of drugs taken into phase 2 are effective, roughly 20% of phase 3 studies, if appropriately powered, should have yielded positive results. Given this, we then explored study size in AHF research, as a potential explanation for the high failure rate in these studies. Comparison of published phase 2 and 3 clinical trials with registries in AHF suggest that populations in both large and small trials differ from “real life”. Meta-regression models suggest that both control event rates, and in the serelaxin program as an example, treatment effects, decline with increasing study size greatly reducing power (figure). This effect dilution might be explained by an increasing proportion of patients enrolled in studies who cannot benefit from the study drug.

Figure 1. Power at two-sided 0.05 significance level to detect an effect size of hazard ratio of 0.65 (left) or 0.8 (right) with a placebo event rate of 10% (top) and 20% (bottom) at N=100 at various treatment effect dilutions with increasing sample size.

Conclusion

These data suggest that it is unlikely that the very high rate of negative AHF phase III trials can be explained by chance alone. Potentially, our tendency to increase sample size does not necessarily increase statistical power, due to more heterogenous populations leading to reduced event rates and treatment effects.

Transforaminal Insertion of a Thermocouple on the Posterior Vertebral Wall Combined with Hydrodissection during Lumbar Spinal Radiofrequency Ablation

The purpose of the present article is to describe the technique of transforaminal insertion of an ultrathin thermosensor in the anterior epidural space in 13 patients treated by radiofrequency ablation. The mean time taken to position the thermosensor was 10.6 minutes (range, 5-38 minutes). Technical success was 93% (correct positioning in 13/14 levels). Additional hydrodissection was performed through the same access in 11 cases. No postoperative neural deficit was elicited in any of the cases.

Percutaneous extra-spinal cementoplasty in patients with cancer: A systematic review of procedural details and clinical outcomes

PURPOSE

To perform a systematic review of technical details and clinical outcomes of percutaneous extra-spinal cementoplasty in patients with malignant lesions.

MATERIALS AND METHODS

PUBMED, MEDLINE, MEDLINE in-process, EMBASE and the Cochrane databases were searched between January 1990 and February 2019 using the keywords «percutaneous cementoplasty», «percutaneous osteoplasty» and «extra-spinal cementoplasty». Inclusion criteria were: retrospective/prospective cohort with more than 4 patients, published in English language, reporting the use of percutaneous injection of cement inside an extra-spinal bone malignant tumour using a dedicated bone trocar, as a stand-alone procedure or in combination with another percutaneous intervention, in order to provide pain palliation and/or bone consolidation.

RESULTS

Thirty articles involving 652 patients with a total of 761 lesions were reviewed. Mean size of lesion was 45mm (range of mean size among publications: 29-73mm); 489 lesions were located in the pelvis, 262 in the long bones of the limbs and 10 in other locations. Cementoplasty was reported as a stand-alone procedure for 60.1% of lesions, and combined with thermal ablation for 26.2% of lesions, implant devices for 12.3% of lesions, and balloon kyphoplasty for 1.4% of lesions. The mean volume of injected cement was 8.8mL (range of mean volume among publications: 2.7-32.2mL). The preoperative visual analogic scores ranged between 3.2 and 9.5. Postoperative scores at last available follow-up ranged from 0.4 to 5.6. Thirteen papers reported a reduction of the visual analogic scores of 5 points or more. Nerve injury was the most frequent symptomatic leakage (0.6%).

CONCLUSION

Percutaneous extra-spinal cementopasty is predominantly performed as a stand-alone procedure and for lesions in the bony pelvis. It appears to be an effective tool to manage pain associated with malignant bone tumours. There is however a lack of standardization of the technique among the different publications.

A study on discrete and discrete fractional pharmacokinetics-pharmacodynamics models for tumor growth and anti-cancer effects

We study the discrete and discrete fractional representation of a pharmacokinetics - pharmacodynamics (PK-PD) model describing tumor growth and anti-cancer effects in continuous time considering a time scale h ℕ 0 h $h\mathbb{N}_0^h$ , where h > 0. Since the measurements of the tumor volume in mice were taken daily, we consider h = 1 and obtain the model in discrete time (i.e. daily). We then continue with fractionalizing the discrete nabla operator to obtain the model as a system of nabla fractional difference equations. The nabla fractional difference operator is considered in the sense of Riemann-Liouville definition of the fractional derivative. In order to solve the fractional discrete system analytically we state and prove some theorems in the theory of discrete fractional calculus. For the data fitting purpose, we use a new developed method which is known as an improved version of the partial sum method to estimate the parameters for discrete and discrete fractional models. Sensitivity analysis is conducted to incorporate uncertainty/noise into the model. We employ both frequentist approach and Bayesian method to construct 90 percent confidence intervals for the parameters. Lastly, for the purpose of practicality, we test the discrete models for their efficiency and illustrate their current limitations for application.

Preclinical evaluation of the atraumatic nature of a spring loaded blunt tip coaxial needle in a swine model

PURPOSE

To test in vivo in an animal model the inherent atraumatic characteristics of the spring loaded blunt tip of a coaxial needle (Gangi-SoftGuard®, Apriomed, Sweden) against a conventional sharp stylet coaxial needle.

MATERIAL AND METHODS

The study was conducted on a 40kg male swine that was its own control for a vascular trauma model. The procedure consisted of voluntary attempts to transfix and traverse the artery/aorta under continuous real-time angiogram. Test and control needles were positioned in the region of the intercostal, superior mesenteric and femoral/deep femoral arteries, and in the aorta. Computed tomography (CT) angiogram was performed post trauma to check for bleeding in the form of extravasation of contrast material. One attempt was performed per site and needle, except for the intercostal artery where a second attempt was done with the test needle, resulting in a total of 4 and 5 tests for the control and test needles, respectively.

RESULTS

With the spring loaded blunt tip, no vascular trauma or bleeding was noted in the intercostal, superior mesenteric and femoral arteries, nor in the aorta. Vascular spasm that recovered with time was noted during the second attempt to transfix the same intercostal artery. There were consistent vascular traumas and bleedings with the control needle in all three tested arteries and the aorta, confirmed on angiogram as well as CT angiogram.

CONCLUSION

The atraumatic feature offered by the spring loaded blunt tip prevented vascular trauma during the 5 attempts made to transfix the artery/aorta in a swine.

Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning

BACKGROUND

Machine learning models may enhance the early detection of clinically relevant hyperbilirubinemia based on patient information available in every hospital.

METHODS

We conducted a longitudinal study on preterm and term born neonates with serial measurements of total serum bilirubin in the first two weeks of life. An ensemble, that combines a logistic regression with a random forest classifier, was trained to discriminate between the two classes phototherapy treatment vs. no treatment.

RESULTS

Of 362 neonates included in this study, 98 had a phototherapy treatment, which our model was able to predict up to 48 h in advance with an area under the ROC-curve of 95.20%. From a set of 44 variables, including potential laboratory and clinical confounders, a subset of just four (bilirubin, weight, gestational age, hours since birth) suffices for a strong predictive performance. The resulting early phototherapy prediction tool (EPPT) is provided as an open web application.

CONCLUSION

Early detection of clinically relevant hyperbilirubinemia can be enhanced by the application of machine learning. Existing guidelines can be further improved to optimize timing of bilirubin measurements to avoid toxic hyperbilirubinemia in high-risk patients while minimizing unneeded measurements in neonates who are at low risk.

Methadone dosing strategies in preterm neonates can be simplified

AIMS

A dramatic increase in newborn infants with neonatal abstinence syndrome has been observed and these neonates are frequently treated with complex methadone dosing schemes to control their withdrawal symptoms. Despite its abundant use, hardly any data on the pharmacokinetics (PK) of methadone is available in preterm neonates. Therefore we investigated developmental PK of methadone and evaluated current dosing strategies and possible simplification in this vulnerable population.

METHODS

A single-centre open-label prospective study was performed to collect PK data after a single oral dose of methadone in preterm neonates. A population PK model was built to characterize developmental PK of (R)- and (S)-methadone. Model-based simulations were performed to identify a simplified dosing strategy to reach and maintain target methadone exposure.

RESULTS

A total of 121 methadone concentrations were collected from 31 preterm neonates. A one-compartment model with first order absorption and elimination kinetics best described PK data for (R)- and (S)-methadone. Clearance increases with advancing gestational age and differs between R- and S-enantiomer, being slightly higher for the former (0.244 vs 0.167 L/h). Preterm neonates reached target exposure after 48 hours with currently used dosing schedules. Output from simulations revealed that target exposures can be achieved with a simplified dosing strategy during the first 4 days of treatment.

CONCLUSION

Methadone clearance in preterm neonates increases with advancing gestational age and its disposition is influenced by its chirality. Simulations that account for developmental PK changes indicate a shorter methadone dosing strategy can maintain target exposure to control withdrawal symptoms.

Genetic relationship between poultry and wild bird viruses during the highly pathogenic avian influenza H5N6 epidemic in the Netherlands, 2017-2018

In the Netherlands, three commercial poultry farms and two hobby holdings were infected with highly pathogenic avian influenza (HPAI) H5N6 virus in the winter of 2017-2018. This H5N6 virus is a reassortant of HPAI H5N8 clade 2.3.4.4 group B viruses detected in Eurasia in 2016. H5N6 viruses were also detected in several dead wild birds during the winter. However, wild bird mortality was limited compared to the caused by the H5N8 group B virus in 2016-2017. H5N6 virus was not detected in wild birds after March, but in late summer infected wild birds were found again. In this study, the complete genome sequences of poultry and wild bird viruses were determined to study their genetic relationship. Genetic analysis showed that the outbreaks in poultry were not the result of farm-to-farm transmissions, but rather resulted from separate introductions from wild birds. Wild birds infected with viruses related to the first outbreak in poultry were found at short distances from the farm, within a short time frame. However, no wild bird viruses related to outbreaks 2 and 3 were detected. The H5N6 virus isolated in summer shares a common ancestor with the virus detected in outbreak 1. This suggests long-term circulation of H5N6 virus in the local wild bird population. In addition, the pathogenicity of H5N6 virus in ducks was determined, and compared to that of H5N8 viruses detected in 2014 and 2016. A similar high pathogenicity was measured for H5N6 and H5N8 group B viruses, suggesting that biological or ecological factors in the wild bird population may have affected the mortality rates during the H5N6 epidemic. These observations suggest different infection dynamics for the H5N6 and H5N8 group B viruses in the wild bird population.

Target-Mediated Drug Disposition Model for Bispecific Antibodies: Properties, Approximation, and Optimal Dosing Strategy

Bispecific antibodies (BsAbs) bind to two different targets, and create two binary and one ternary complex (TC). These molecules have shown promise as immuno-oncology drugs, and the TC is considered the pharmacologically active species that drives their pharmacodynamic effect. Here, we have presented a general target-mediated drug disposition (TMDD) model for these BsAbs, which bind to two different targets on different cell membranes. The model includes four different binding events for BsAbs, turnover of the targets, and internalization of the complexes. In addition, a quasi-equilibrium (QE) approximation with decreased number of binding parameters and, if necessary, reduced internalization parameters is presented. The model is further used to investigate the kinetics of BsAb and TC concentrations. Our analysis shows that larger doses of BsAbs may delay the build-up of the TC. Consequently, a method to compute the optimal dosing strategy of BsAbs, which will immediately create and maintain maximal possible TC concentration, is presented.

Innervation of flexor hallucis longus muscle: an anatomical study for selective neurotomy

BACKGROUND

The aim of the study was to describe the innervation of flexor hallucis longus (FHL) and obtain its surgical coordinates to facilitate selective neurotomy.

MATERIALS AND METHODS

Fifteen embalmed lower limbs of adults were studied. Anatomical dissections to isolate the innervating branches of FHL were performed. Distance between the supplying nerve of FHL, including both its origin and termination, and the medial malleolus were obtained, providing anatomical coordinates beneficial for surgery.

RESULTS

In all cases, FHL was innervated by only one branch, which originated from the tibial nerve. Mean distance between the medial malleolus and the nervous branch origin was 21.39 ± 3.05 cm. Mean distance between the medial malleolus and the nervous branch termination was 12.7 ± 1.59 cm. Length of the nervous branch innervating FHL was proportional to the length of the leg, measuring 8.69 ± 2.45 cm. All nerves were located 15-17.4 cm above the medial malleolus.

CONCLUSIONS

This anatomical study traced valuable surgical coordinates useful for performing selective peripheral neurotomy on the nerve branch innervating the FHL.

Clinical Pharmacology and Pharmacometrics to Better Understand Physiological Changes During Pregnancy and Neonatal Life

Pregnant women, fetuses, and newborns are particularly vulnerable patient populations. During pregnancy, the body is subject to physiological changes that influence the pharmacokinetics and pharmacodynamics of drugs. Inappropriate dosing in pregnant women can result in sub-therapeutic or toxic effects, putting not only the pregnant woman but also her fetus at risk. During neonatal life, maturation processes also affect pharmacokinetics and pharmacodynamics of drugs. Inappropriate dosing in newborns leads not only to short-term complications but can also have a negative impact on the long-term development of infants and children. For these reasons, it is crucial to characterize physiological changes in pregnant women, describe placental transfer kinetics of drugs, and describe physiological changes related to the transition from intrauterine to extrauterine life and maturation processes in preterm and term neonates. Quantitative pharmacological approaches such as pharmacometric and physiologically-based modeling and model-based simulations can be useful to better understand and predict such physiological changes and their effects on drug exposure and response. This review article (1) gives an overview of physiological changes in pregnant women, their fetuses, and (pre)term neonates, (2) presents case studies to illustrate applications of new modeling and simulation approaches, and (3) discusses challenges and opportunities in optimizing and personalizing treatments during pregnancy and neonatal life.

Age-related changes in brain deactivation but not in activation after motor learning

It is poorly understood how healthy aging affects neural mechanisms underlying motor learning. We used blood-oxygen-level dependent (BOLD) contrasts to examine age-related changes in brain activation after acquisition and consolidation (24 h) of a visuomotor tracking skill. Additionally, structural magnetic resonance imaging and diffusion tensor imaging were used to examine age-related structural changes in the brain. Older adults had reduced gray matter volume (628 ± 57 ml) and mean white matter anisotropy (0.18 ± 0.03) compared with young adults (741 ± 59 ml and 0.22 ± 0.02, respectively). Although motor performance was 53% lower in older (n = 15, mean age 63.1 years) compared with young adults (n = 15, mean age 25.5 years), motor practice improved motor performance similarly in both age groups. While executing the task, older adults showed in general greater brain activation compared with young adults. BOLD activation decreased in parietal and occipital areas after skill acquisition but activation increased in these areas after consolidation in both age groups, indicating more efficient visuospatial processing immediately after skill acquisition. Changes in deactivation in specific areas were age-dependent after consolidating the motor skill into motor memory. Young adults showed greater deactivations from post-test to retention in parietal, occipital and temporal cortices, whereas older adults showed smaller deactivation in the frontal cortex. Since learning rate was similar between age groups, age-related changes in activation patterns may be interpreted as a compensatory mechanism for age-related structural decline.

Characterization of Maternal and Neonatal Pharmacokinetic Behavior of Ceftazidime

Ceftazidime is a parenteral cephalosporin frequently used in pregnant women for treatment of urinary tract or intrauterine infections. Despite its regular use in pregnant women, ceftazidime disposition in both mother and fetus is not well understood, and a pharmacokinetic (PK) model that allows characterization and simulation of both maternal and preterm neonatal ceftazidime disposition is not available. In this study, 10 pregnant women with suspected or proven intrauterine infections in the late second and early third trimester were treated with 1 g of ceftazidime intravenously every 6 hours. During ceftazidime treatment, one maternal and umbilical cord blood sample was taken at delivery to quantify ceftazidime concentrations in the mother and preterm neonate. Data showed that ceftazidime concentrations in the mother were comparable to those observed in the neonate. Based on these data, a PK model was developed to describe maternal disposition, ceftazidime distribution over the placenta, and elimination in the neonate. The maternal substructure of the model was parameterized according to a previously reported ceftazidime model with minor adjustments to account for pregnancy-related effects on renal elimination of ceftazidime. The expanded population PK model with an additional neonatal compartment was fitted to measured drug concentrations in the neonate. The neonatal elimination rate constant at delivery was close to that estimated for the mother. The presented results show that ceftazidime readily crosses the placenta and indicate that perinatal PK behavior of ceftazidime in preterm neonates can be expected to be similar to those observed in their mothers.

Release and Decay Kinetics of Copeptin vs AVP in Response to Osmotic Alterations in Healthy Volunteers

CONTEXT

Copeptin is the C-terminal fragment of the arginine vasopressin (AVP) prohormone whose measurement is more robust than that of AVP. Similar release and clearance characteristics have been suggested promoting copeptin as a surrogate marker.

OBJECTIVE

To characterize the physiology of osmotically regulated copeptin release and its half-life in direct comparison with plasma AVP.

DESIGN

Ninety-one healthy volunteers underwent a standardized three-phase test protocol including (1) osmotic stimulation into the hypertonic range by hypertonic-saline infusion followed by osmotic suppression via (2) oral water load and (3) subsequent glucose infusion. Plasma copeptin, AVP, serum sodium, and osmolality levels were measured in regular intervals.

RESULTS

In phase 1, an increase in median osmotic pressure [289 (286; 291) to 311 (309; 314) mOsm/kg H2O] caused similar release kinetics of plasma copeptin [4 (3.1; 6) to 29.3 (18.6; 48.2) pmol/L] and AVP [1 (0.7; 1.6) to 10.3 (6.8; 18.8) pg/mL]. Subsequent osmotic suppression to 298 (295; 301) mOsm/kg at the end of phase 3 revealed markedly different decay kinetics between both peptides-an estimated initial half-life of copeptin being approximately 2 times longer than that of AVP (26 vs 12 minutes).

CONCLUSION

Copeptin is released in equimolar amounts with AVP in response to osmotic stimulation, suggesting its high potential as an AVP surrogate for differentiation of osmotic disorders. Furthermore, we here describe the decay kinetics of copeptin in response to osmotic depression enabling to identify a half-life for copeptin in direct comparison with AVP.