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Ayurveda
Published in Dilip Ghosh, Pulok K. Mukherjee, Natural Medicines, 2019
Subhadip Banerjee, Sayan Biswas, Pulok K. Mukherjee
The philosophy of treating a system or body as a whole is gaining relevance as we look back or evolve towards a ‘systems’ approach in this post-genomic era. After so much technological boom, our understanding of Ayurveda is only at its beginning. What seems a philosophy in common sense may be found to have in-depth scientific nuances on exploration. Chemical standardisation like biomarker analysis and metabolomic profiling has unfolded a diverse chemical space of safe and therapeutically relevant drugs. Research on Ayurgenomics is adding evidence regarding the genomic correlates of Vata, Pitta and Kapha, the three principle bio-factors termed as Tridosha (bodily humours; Govindaraj et al. 2015). Exploring molecular the pharmacology of intelligent synergistic traditional formulations to elucidate and validate safety, toxicity, pharmacokinetics, metabolic stability like herb–drug interactions is gaining importance. Next-generation approaches like ‘network or systems pharmacology’ are the tools of these efforts. Deciphering the novel mechanism to ensure harmony inside a system’s signalling (Vata), metabolism (Pitta) and storage (Kapha) – called the balance of the doshas – presents a real challenge (Hopkins 2008; Chandran et al. 2015). However, we still need to focus on the validation of traditional claims and practices mentioned in Ayurveda like Panchakarma, Agnikarma, Rasayana, which require in-depth scientific exploration (Debnath et al. 2015).
A Regulatory View on Dose-Finding Studies and on the Value of Dose–Exposure–Response Analysis
Published in John O’Quigley, Alexia Iasonos, Björn Bornkamp, Handbook of Methods for Designing, Monitoring, and Analyzing Dose-Finding Trials, 2017
Sofia Friberg Hietala, Efthymios Manolis, Flora Musuamba Tshinanu
At the extremes of the age range, i.e., pediatrics and geriatrics, both PK and PD may be altered in relation to the typical adult patient. Initial pediatric dosing should be guided using an adequately supported adult PK/PD model but taking into account growth and maturation effects on PK and PD. The model needs to be informed not only by data on the chemical entity in question but also from previous developments using systems pharmacology and PBPK. The need for confirmatory pediatric studies other than PK/PD depends on the disease, endpoints, and the quality of the PK/PD model and the confidence in the assumptions associated with the model.
Statistical Approaches in the Development of Digital Therapeutics
Published in Oleksandr Sverdlov, Joris van Dam, Digital Therapeutics, 2023
Oleksandr Sverdlov, Yevgen Ryeznik, Sergei Leonov, Valerii Fedorov
Let us consider the problem of modeling dynamics of physiological or neurobehavioral processes in patients with a neuropsychiatric disorder. There may be massive data per subject: demographic and baseline characteristics, clinical questionnaire outcomes, medical prescription information, high-frequency data generated by digital technologies such as wearable devices and smartphones, etc. Data from personal digital devices may be accrued over days or even months, at it may be highly unstructured. Such “big data” holds significant promise and value. Still, it must be analyzed judiciously to avoid “black box” predictions without a scientific explanation of the underlying mechanisms of the phenomena of interest. Understanding the causality of input–output relationships is essential in biomedical and clinical sciences. Ideally, mechanistic models such as systems biology, systems pharmacology, and PK/PD models should form the basis for developing new drugs and biologics. In some diseases, such as type 1 diabetes, there are good mechanistic models describing the metabolism dynamics. However, in other areas such as neuropsychiatric disorders, disease mechanisms are more complex, and mechanistic models are much more elusive. Data science and predictive analytics provide powerful tools for finding and extrapolating/forecasting important trends and data patterns. One may be tempted to apply these data-driven methods to complex problems such as predicting disease trajectory in neuropsychiatry, bypassing the step of obtaining causal mechanistic models. However, is this justifiable in this setting, and if so, to what extent?
Introduction to biological complexity as a missing link in drug discovery
Published in Expert Opinion on Drug Discovery, 2018
Gary A. Gintant, Christopher H. George
Figure 1 depicts a schematized framework of “quantitative and systems pharmacology” (QSP) that integrates information emerging at multiple scales using the idea of “horizontal” and “vertical” network architecture [8,9]. Erwin Chargaff’s prediction in the context of genetic engineering that “If you can modify a cell, it’s only a short step to modifying a mouse, and if you can modify a mouse, it’s only a step to modifying a higher animal, even man” [10] may hold true conceptually, but it does not fully recognize the nature of all of the components—some quantifiable, some not—involved in “scaling up.” There is an essential need to understand the basis of the increased complexity associated with transitions from the molecular scale through to organisms and beyond that affects the fidelity of translation to the clinic; such transitions are chasms, not short steps and not unlike the “Valley of Death” phrase used to describe the arduous transition of drugs from laboratory findings to successful human clinical trials.
Mathematical modeling of efficacy and safety for anticancer drugs clinical development
Published in Expert Opinion on Drug Discovery, 2018
Silvia Maria Lavezzi, Elisa Borella, Letizia Carrara, Giuseppe De Nicolao, Paolo Magni, Italo Poggesi
In the near future, mathematical models should embrace the complexity of the interactions between tumor, patient and drug, and become an instrument for optimizing the tradeoff between efficacy and safety [44]. Among the more mechanistic systems models that have the potential to increase predictive capabilities, one may mention network-based systems pharmacology models. Mathematical descriptions of signaling pathways and/or chemical reactions at the microscopic (e.g. cellular) scale may be considered for inclusion in whole body physiologically based PK models. Examples of cellular-scale models can be found for instance in [112], where the development of malignancy in ductal carcinoma in situ was explored, and in [113], where an ODE cellular model was used to explore the hypothesis that extracellular pH normalization can reduce tumor’s invasion. Systems pharmacology approaches can help elucidating mechanisms that drive drug efficacy, as well as identifying off-target toxicities (hence explaining undesired effects) [114], and, in the end, are a valuable resource to face the challenges connected to targeted drug development and personalized medicine.
Advances in omics for informed pharmaceutical research and development in the era of systems medicine
Published in Expert Opinion on Drug Discovery, 2018
Jane P. F. Bai, Ioannis N. Melas, Junguk Hur, Ellen Guo
Omics technologies are an integral part of informed pharmaceutical R&D, and their role in R&D will continue to expand. There are still challenges to be addressed to fully utilize omics technologies, especially for treating complex diseases, such as neurological and autoimmune diseases. Just to name a few. How to establish omics data standards to reduce inter-laboratory variability and to increase confidence in distinctly classifying disease subtypes to aid the design of clinical trials? How to translationally bridge omics data in pathological conditions and clinical phenotypes of individual diseases? How to integrate multiple layers of omics information and phenotypic characterizations, including pathological biomarkers of a disease, pharmacodynamics responses to a drug treatment? How to link omics data to brain images or cognitive scores for neurological or psychological diseases? Computational and statistical methods will undoubtedly continue to play a role. Looking ahead, quantitative systems pharmacology may help bridge the gap. It is anticipated that the molecular networks described by omics data and the physiological/pathological networks presented by clinical tests/diagnosis will be quantitatively integrated to inform R&D.