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Algorithms as emerging policy tools in genomic medicine
Published in Ulrik Kihlbom, Mats G. Hansson, Silke Schicktanz, Ethical, Social and Psychological Impacts of Genomic Risk Communication, 2020
Progress in AI is likely to raise expectations about the fulfilment of individualized medical treatments, commonly referred to as ‘personalized medicine’ (Hedgecoe, 2004; Prainsack, 2017). As a move away from a ‘one size fits all’ approach to medicine, personalized medicine (and the related notions of ‘individualized’ and ‘stratified’ medicines) captures the particularities of a patient’s disease or predisposition to the disease. Patterns are identified by combining and analysing information about key variables, such as genome and lifestyle. In this context, predictive computerized systems rely on access to large datasets that will then be analysed computationally, i.e. ‘ “Big Data”: getting more data, of better quality, from more people (…). Proponents of personalized medicine require access to data from large-scale genomic research projects’ (Hoeyer, 2016, p. 532). In the same vein, Adjekum et al. (2017) suggest that: ‘To a large extent, precision medicine is driven by three main concurrent trends: the increasing availability of heterogeneous large-scale databases from which novel patient aggregates evolve, advances in the characterization of medically relevant information, and novel computational tools for data analytics’ (p. 704).
Key concepts in pulmonary rehabilitation
Published in Claudio F. Donner, Nicolino Ambrosino, Roger S. Goldstein, Pulmonary Rehabilitation, 2020
Felipe V.C. Machado, Frits M.E. Franssen, Martijn A. Spruit
‘Personalized medicine’ refers to the tailoring of medical treatment to the individual characteristics of each patient. This requires the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease (or condition) or their response to a specific treatment (83). These can be applied in chronic respiratory diseases by identifying clinically meaningful and useful subgroups or phenotypes that exhibit similar underlying biologic or physiologic mechanism, clinical outcomes and therapeutic response profiles (84,85). For example, in asthma it has been proposed that patients may be stratified into several phenotypes based on the type of airway inflammation (supported by different molecular pathways), and the identification of severe eosinophilic asthma has been shown to select patients that present good clinical response to a specific pharmacological treatment (86).
Biostatistics
Published in Arkadiy Pitman, Oleksandr Sverdlov, L. Bruce Pearce, Mathematical and Statistical Skills in the Biopharmaceutical Industry, 2019
Arkadiy Pitman, Oleksandr Sverdlov, L. Bruce Pearce
Master protocols is a novel and very useful concept in drug development. Modern clinical research is increasingly focused around the idea of developing personalized medicine (finding the right treatment for the right patient at the right time). This is particularly important in oncology where carcinogenesis and treatment are closely related to molecular and genetic mutations. Developing treatments for personalized medicine is a big challenge, because the number of potential options (different therapies and their combinations) may be huge and the patient populations may be small. There is a strong need for clever designs to evaluate multiple therapies across a spectrum of indications in heterogeneous patient populations.
Combinations of chemo-, immuno-, and gene therapies using nanocarriers as a multifunctional drug platform
Published in Expert Opinion on Drug Delivery, 2022
Caroline Hopkins, Kaila Javius-Jones, Yixin Wang, Heejoo Hong, Quanyin Hu, Seungpyo Hong
The insolubility and hydrophobic nature of chemotherapeutics require cytotoxic solvents for dissolution and subsequent injection, thus increasing the cytotoxicity of already cytotoxic drugs. The inherent lack of cell specificity of small-molecule drugs increases occurrences of side effects, which support patients to prematurely discontinue treatment [66]. This problem is effectively negated by encapsulation within nanocarriers for release only once inside the tumor microenvironment. The use of nanocarriers in cancer treatment will allow for decreased systemic toxicity, by specifically directing immunotherapeutics, drugs, and/or genes to the tumor site. Although controversial, this is possible simply by passive targeting via the EPR effect. Furthermore, the addition of targeting ligands on the nanocarrier surface can further improve tumor specificity of the delivery systems and reduce toxicity attributed to off-target effects. The precise control based on both passive and active targeting would ultimately enable us to engineer tailored nanocarriers that individually address the needs of each patient. As cancer therapy evolves, personalized medicine will become routine, tailoring treatments to a patient’s specific cancer cell type.
Current Perspective in the Management of Hepatocellular Carcinoma: Time to Get Personal!
Published in Journal of Investigative Surgery, 2021
This last point highlights some of the existing obstacles in the pursuit of personalized medicine. Specifically, there is the need to be able to accumulate and evaluate significant amounts of data with the use of genetic databases, such as the Cancer Genome Atlas in this paper. The use and analysis of “Big Data” necessitates the integration of machine learning and artificial intelligence as an essential tool in the fight against cancer. Additionally, obtaining these huge amounts of data is not always easy given the scarcity of tissue, especially for advanced disease. The use of liquid biopsies could potentially improve the accumulation of circulating biomarkers, although what is really needed are better preclinical models, which will involve cell cultures that would take into consideration the tumor microenvironment.
Pharmacogenetic and pharmaco-miR biomarkers for tailoring and monitoring myasthenia gravis treatments
Published in Expert Review of Precision Medicine and Drug Development, 2020
Paola Cavalcante, Renato Mantegazza, Pia Bernasconi
Pharmacogenetics, focused on candidate genes, and pharmacogenomics, encompassing all genome, are widely recognized as fundamental disciplines to gain knowledge for developing personalized medicine. Genetic factors associated with drug responsiveness and toxicity are the most reliable markers for tailoring therapies. In particular, genotyping of SNPs known to influence the drug effects is the most feasible and consistent approach for therapy determination before starting a treatment. However, the genetic profile associated with treatment response can significantly differ between disease populations of different ethnicity, and this is an important issue to be considered when attempting to extend and apply the results of pharmacogenetic/pharmacogenomic studies to patients of other populations. A number of pharmacogenetic biomarkers useful for tailoring conventional immunosuppressive (e.g. glucocorticoids, azathioprine, tacrolimus) drug therapies to individual patients has been recognized in considerable reports performed in MG or other pathological conditions, as above described. Nevertheless, this translational research field still needs to be expanded, to gain a general consensus on the key variants to be tested for individualizing the approaches for MG in different populations. This is even more urgent in view of the implementation into the therapeutic MG algorithm of biological drugs, for which pharmacogenetic profiling is lacking.