Development and Evaluation of a Physiologically Based Pharmacokinetic (PBPK) Population Model for Elderly Individuals

Clinical drug development is traditionally focused on young and middle-aged adults. The elderly are often underrepresented in clinical trials, even though persons aged 65 years and older receive the majority of drug prescriptions. Consequently, there is a knowledge gap on dose-exposure relationships in elderly subjects. This thesis aimed at contributing to a better understanding of the age-related mechanisms governing the pharmacokinetics (PK) in this clinically understudied population. First, a physiologically based pharmacokinetic (PBPK) database for the course of healthy ageing was successfully established. For parameterization of the PBPK model for healthy ageing individuals, anthropometric and physiological data were identified in the literature, which were incorporated into the PBPK software PK-Sim®. Although age-related changes occurring from 65 to 100 years of age were the main focus of this work, data on anatomical and physiological changes beginning from early adulthood to the elderly age range were also included for a sound and continuous description of ageing humans. In total, 118 studies comprising 47029 male and 67419 female subjects were included to build the elderly PBPK database. As next step, the capability of the elderly PBPK approach to predict the distribution and elimination of drugs was verified using the test compounds morphine and furosemide administered intravenously. Both drugs are cleared by a single elimination pathway. PK parameters for the two compounds in younger adults and elderly individuals were obtained from the literature. Matching virtual populations – with regard to age, gender, anthropometric measures and dosage – were generated. Profiles of plasma drug concentration over time, volume of distribution at steady-state (Vss) and elimination half-lives (t1/2) from the literature were compared to those predicted by PBPK simulations, for both younger adults and the elderly. Based on age-informed physiology, the predicted PK profiles described age-associated trends well. The root mean squared prediction error (RMSE) for the prediction of plasma concentrations for furosemide and morphine in the elderly was improved by 32% and 49%, respectively, compared to predictions without age-informed physiology. The majority of the individual Vss and t1/2 values of the two model compounds furosemide and morphine were well predicted in the elderly population, except for long furosemide half-lives. Finally, the reliability of predictions outside the tested adult age range towards the extremes of ages was assessed using the multi-elimination pathway compound ciprofloxacin as probe drug. Mean data of 69 published clinical trials were identified and used for model building, simulation as well as the verification process. The predictive performance on both ends of the age scale was assessed using individual data of 236 pediatric and 22 geriatric patients observed in clinical trials. Ciprofloxacin model verification demonstrated no concentration-related bias and accurate predictions for the adult age range with only 4.8 % of the mean observed concentrations following intravenous and 12.1 % following oral administration outside the simulated 2-fold range. Predictions towards both extremes of ages for the area under the plasma concentration–time curve (AUC) and the maximum plasma concentration (Cmax) were reliable. The results of this thesis support the feasibility of using a knowledge-driven PBPK ageing model to predict PK alterations throughout the entire course of ageing, and thus to optimize drug therapy also in older adult individuals. Overall, the predictive power of a thoroughly informed middle-out approach towards older adults to potentially support the decision making process for pharmacotherapy in the elderly was demonstrated. These results indicate that medication safety in geriatric patients may be greatly facilitated by the information gained from PBPK predictions

UR:BAN KA-WER: Accident data analysis and pre-crash simulation for the configuration and assessment of driver assistance systems in urban scenarios

The project UR:BAN "Cognitive assistance (KA)" aims at developing future assistance systems providing improved performance in complex city traffic. New state-of-the-art panoramic sensor technologies now allow comprehensive monitoring and evaluation of the vehicle environment. In order to improve protection of vulnerable road users such as pedestrians and cyclists, a particular objective of UR:BAN is the evaluation and prediction of their behaviour and actions. The objective of subproject "WER" is development support by providing quantitative estimates of traffic collisions at the very start and predict potential in terms of optimized accident avoidance and reduction of injury severity. For this purpose an integrated computer simulation toolkit is being devised based on real world accidents (GIDAS as well as video documented accidents), allowing the prediction of potential effectiveness and future benefit of assistance systems in this accident scenario. Subsequently, this toolkit may be used for optimizing the design of implemented assistance systems for improved effectiveness

The Bigger Fish: A Comparison of Meta-Learning QSAR Models on Low-Resourced Aquatic Toxicity Regression Tasks.

Toxicological information as needed for risk assessments of chemical compounds is often sparse. Unfortunately, gathering new toxicological information experimentally often involves animal testing. Simulated alternatives, e.g., quantitative structure – activity relationship (QSAR) models, are preferred to infer the toxicity of new compounds. Aquatic toxicity data collections consist of many related tasks, each predicting the toxicity of new compounds on a given species. Since many of these tasks are inherently low-resource, i.e., involve few associated compounds, this is challenging. Meta-learning is a subfield of artificial intelligence that can lead to more accurate models by enabling the utilization of information across tasks. In our work, we benchmark various state-of-the-art meta-learning techniques for building QSAR models, focusing on knowledge sharing between species. Specifically, we employ and compare transformational machine learning, model-agnostic meta-learning, fine-tuning, and multi-task models. Our experiments show that established knowledge-sharing techniques outperform single-task approaches. We recommend the use of multi task random forest models for aquatic toxicity modeling, which matched or exceeded the performance of other approaches and robustly produced good results in the low-resource settings we studied. This model functions on a species level, predicting toxicity for multiple species across various phyla, with flexible exposure duration and on a large chemical applicability domain.Environmental Biolog

Transformational Machine Learning

a novel ML metho

Open Systems Pharmacology Community-An Open Access, Open Source, Open Science Approach to Modeling and Simulation in Pharmaceutical Sciences

Systems pharmacology integrates structural biological and pharmacological knowledge and experimental data, enabling dissection of organism and drug properties and providing excellent predictivity. The development of systems pharmacology models is a significant task requiring massive amounts of background information beyond individual trial data. The qualification of models needs repetitive demonstration of successful predictions. Open Systems Pharmacology is a community that develops, qualifies, and shares professional open source software tools and models in a collaborative open-science way

Recombinant Soluble Respiratory Syncytial Virus F Protein That Lacks Heptad Repeat B, Contains a GCN4 Trimerization Motif and Is Not Cleaved Displays Prefusion-Like Characteristics

The respiratory syncytial virus (RSV) fusion protein F is considered an attractive vaccine candidate especially in its prefusion conformation. We studied whether recombinant soluble RSV F proteins could be stabilized in a prefusion-like conformation by mutation of heptad repeat B (HRB). The results show that soluble, trimeric, non-cleaved RSV F protein, produced by expression of the furin cleavage site-mutated F ectodomain extended with a GCN4 trimerization sequence, is efficiently recognized by pre- as well as postfusion-specific antibodies. In contrast, a similar F protein completely lacking HRB displayed high reactivity with prefusion-specific antibodies recognizing antigenic site Ø, but did not expose postfusion-specific antigenic site I, in agreement with this protein maintaining a prefusion-like conformation. These features were dependent on the presence of the GCN4 trimerization domain. Absence of cleavage also contributed to binding of prefusion-specific antibodies. Similar antibody reactivity profiles were observed when the prefusion form of F was stabilized by the introduction of cysteine pairs in HRB. To study whether the inability to form the 6HB was responsible for the prefusion-like antibody reactivity profile, alanine mutations were introduced in HRB. Although introduction of alanine residues in HRB inhibited the formation of the 6HB, the exposure of postfusion-specific antigenic site I was not prevented. In conclusion, proteins that are not able to form the 6HB, due to mutation of HRB, may still display postfusion-specific antigenic site I. Replacement of HRB by the GCN4 trimerization domain in a non-cleaved soluble F protein resulted, however, in a protein with prefusion-like characteristics, suggesting that this HRB-lacking protein may represent a potential prefusion F protein subunit vaccine candidate