Quantitative Predictive Modelling Approaches to Understanding Rheumatoid Arthritis:A Brief Review

Rheumatoid arthritis is a chronic autoimmune disease that is a major public health challenge. The disease is characterised by inflammation of synovial joints and cartilage erosion, which lead to chronic pain, poor life quality and, in some cases, mortality. Understanding the biological mechanisms behind the progression of the disease, as well as developing new methods for quantitative predictions of disease progression in the presence/absence of various therapies is important for the success of therapeutic approaches. The aim of this study is to review various quantitative predictive modelling approaches for understanding rheumatoid arthritis. To this end, we start by briefly discussing the biology of this disease and some current treatment approaches, as well as emphasising some of the open problems in the field. Then, we review various mathematical mechanistic models derived to address some of these open problems. We discuss models that investigate the biological mechanisms behind the progression of the disease, as well as pharmacokinetic and pharmacodynamic models for various drug therapies. Furthermore, we highlight models aimed at optimising the costs of the treatments while taking into consideration the evolution of the disease and potential complications.Publisher PDFPeer reviewe

Quantitative approach for cardiac risk assessment and interpretation in tuberculosis drug development

Cardiotoxicity is among the top drug safety concerns, and is of specific interest in tuberculosis, where this is a known or potential adverse event of current and emerging treatment regimens. As there is a need for a tool, beyond the QT interval, to quantify cardiotoxicity early in drug development, an empirical decision tree based classifier was developed to predict the risk of Torsades de pointes (TdP). The cardiac risk algorithm was developed using pseudo-electrocardiogram (ECG) outputs derived from cardiac myocyte electromechanical model simulations of increasing concentrations of 96 reference compounds which represented a range of clinical TdP risk. The algorithm correctly classified 89% of reference compounds with moderate sensitivity and high specificity (71 and 96%, respectively) as well as 10 out of 12 external validation compounds and the anti-TB drugs moxifloxacin and bedaquiline. The cardiac risk algorithm is suitable to help inform early drug development decisions in TB and will evolve with the addition of emerging data

Developing pharmacokinetic models for nano drug delivery systems

Trabalho Final de Mestrado Integrado, Ciências Farmacêuticas, 2021, Universidade de Lisboa, Faculdade de Farmácia.A área dos nanomedicamentos é interdisciplinar e complexa com fontes de literatura terciárias, sobre a forma de manuais, emergentes desde os 2010 e, ainda assim, os processos que sustentam a farmacocinética e a farmacodinâmica de nanomedicamentos ainda não estão totalmente caracterizados. O objetivo desta monografia é apresentar, para os indivíduos que podem ser relativamente novos na área de nanomedicamentos, as propriedades farmacocinéticas de nanopartículas, as abordagens na modelação farmacocinética, e demonstrar a aplicação destes princípios em exemplos tanto de investigação fundamental, quanto no desenvolvimento e otimização bio galénica de nanomedicamentos. Aqui são descritas as etapas farmacocinéticas de absorção, distribuição, metabolização e eliminação referentes a nanomedicamentos, com realce nos aspetos que distinguem estes processos daquilo que é observado quando se trata de medicamentos “convencionais”. É também fornecida uma discussão sobre conceitos essenciais necessários para discussão de modelação farmacocinética usados nas abordagens compartimentais, mecanísticas, e baseadas na fisiologia. Diversos assuntos tangentes como corrente interesse na área de oncologia, extrapolação interespécies em estudos pré-clínicos e aspetos regulamentares associados são também brevemente abordados. Esta monografia foi realizada com base nas publicações disponíveis nas bases de dados de PubMed e Science Direct até ao mês de setembro do ano 2021. Este trabalho não é único e assemelha-se as revisões de Moss D. M. e Siccardi M., de Glassman P. M. e Muzakantov V. R., ou de Yuan D. et al quanto a organização bem como aos conteúdos.(1–3) A farmacocinética que descreve os medicamentos “convencionais” baseados na distribuição de substâncias ativas começa apenas quando as etapas finais de libertação e degradação das nanopartículas já começam a ocorrer. A existência simultânea de entidades particuladas e moleculares complica a descrição, otimização, desenvolvimento e avaliação regulamentar de novas formulações de nanomedicamentos. Isto, juntamente com a falta de técnicas analíticas adequadas para a quantificação de nanopartículas em meios biológicos, torna os estudos de modelação farmacocinética de nanomedicamentos um desafio.Nanomedicines are a complex and highly interdisciplinary field with recently emerging Textbooks as tertiary literature sources since 2010s, and yet the processes that underpin the pharmacokinetics and pharmacodynamics of nano drug delivery systems are not fully characterized. The aim of this monograph is to introduce the pharmacokinetic dispositions, pharmacokinetic modelling approaches, and to demonstrate application of these principles in examples of both basic research and NDDS development to individuals who may be relatively new to the field of nanomedicine. In this monograph are described the pharmacokinetic steps of absorption, distribution, metabolization and elimination particular to nano drug delivery systems, primarily focusing aspects that distinguish NDDS from “conventional” drugs. A description of essential concepts necessary for discussions of PK modelling in compartmental, mechanistic, and physiology-based approaches are also provided. Various related topics including growing interest in cancer therapy, interspecies extrapolation in pre-clinical study settings, and reglementary affairs related to NDDSs are also briefly addressed. Writing of this monograph was conducted after browsing information available in the PubMed and Science Direct databases up to September 2021. This work is not unique and resembles the reviews by Moss D. M. and Siccardi M., Glassman P. M. and Muzakantov V. R., and Yuan D. et al, in their structure, subject and contents.(1–3) Pharmacokinetics that describes small molecule active substances, begin only when the final steps of nanoparticles fate of release and degradation had begun. Simultaneous existence of both particulate and molecular entities complicates the description, optimization, development, and regulatory assessment of new nano formulations. This together with the lack of appropriate analytical techniques for nanoparticle quantification in biologic media makes pharmacokinetic modelling studies of NDDSs challenging

Two heads are better than one: current landscape of integrating QSP and machine learning

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP ? ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices

Exploiting Clinical Trial Data Drastically Narrows the Window of Possible Solutions to the Problem of Clinical Adaptation of a Multiscale Cancer Model

The development of computational models for simulating tumor growth and response to treatment has gained significant momentum during the last few decades. At the dawn of the era of personalized medicine, providing insight into complex mechanisms involved in cancer and contributing to patient-specific therapy optimization constitute particularly inspiring pursuits. The in silico oncology community is facing the great challenge of effectively translating simulation models into clinical practice, which presupposes a thorough sensitivity analysis, adaptation and validation process based on real clinical data. In this paper, the behavior of a clinically-oriented, multiscale model of solid tumor response to chemotherapy is investigated, using the paradigm of nephroblastoma response to preoperative chemotherapy in the context of the SIOP/GPOH clinical trial. A sorting of the model's parameters according to the magnitude of their effect on the output has unveiled the relative importance of the corresponding biological mechanisms; major impact on the result of therapy is credited to the oxygenation and nutrient availability status of the tumor and the balance between the symmetric and asymmetric modes of stem cell division. The effect of a number of parameter combinations on the extent of chemotherapy-induced tumor shrinkage and on the tumor's growth rate are discussed. A real clinical case of nephroblastoma has served as a proof of principle study case, demonstrating the basics of an ongoing clinical adaptation and validation process. By using clinical data in conjunction with plausible values of model parameters, an excellent fit of the model to the available medical data of the selected nephroblastoma case has been achieved, in terms of both volume reduction and histological constitution of the tumor. In this context, the exploitation of multiscale clinical data drastically narrows the window of possible solutions to the clinical adaptation problem

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 324)

This bibliography lists 200 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during May, 1989. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Design of novel drug delivery system and optimal dosage regimens

Three representative drug delivery systems were analyzed to emphasize the roles of mathematical models and computer-aided simulations in pharmaceutical research. In the first project, a protocol was developed so that the optimal regimen, consisting of the intravenous boluses and subsequent infusion of theophylline, could be obtained once information on the pharmacokinetics became available. The method was based on a two-compartment model of the human body. A module was created and posted on a website for free access. The second project dealt with the transdermal heat-assisted delivery of corticosterone. Heat conduction and drug diffusion through the patch and the skin were expressed in the mathematical model. Four design parameters were estimated. This model was validated using clinical data from the administration of fentanyl. Cortisone concentrations through the patch and skin layers were predicted. The results were used to rank the relative impacts of the design parameters on the corticosterone delivery and to make proper suggestions for fabricating the products. Finally, the simultaneous application of an electric current and soluble microneedles were proposed for the first time. Preliminary experimental studies suggested that the electric field enhanced the flux by increasing drug diffusion and, thereby, the dissolution of the microneedles. One-, two- and three-dimensional simulations were conducted. In addition, protocols were proposed to help with the analysis of laboratory data

Automated system for integration and display of physiological response data

The system analysis approach was applied in a study of physiological systems in both 1-g and weightlessness, for short and long term experiments. A whole body, algorithm developed as the first step in the construction of a total body simulation system is described and an advanced biomedical computer system concept including interactive display/command consoles is discussed. The documentation of the design specifications, design and development studies, and user's instructions (which include program listings) for these delivered end-terms; the reports on the results of many research and feasibility studies; and many subcontract reports are cited in the bibliography