User:Kairui jiang/Personalized Medicine

Among 14 Grand Challenges for Engineering, initiative sponsored by National Academy of Engineering (NAE), personalized medicine has been identified as a key and prospective approach to “achieve optimal individual health decisions”, therefore overcoming the challenge of “Engineer better medicines”.

Development of concept
... personalization measures, including the use of proteomics, imaging analysis, nanoparticle-based theranostics, among others.

Applications
... and more targeted therapy

Diagnosis and intervention
second para: ... and create maximum efficacy with drug prescriptions. For instance, warfarin is the FDA approved oral anticoagulant commonly prescribed to patients with blood clots. Due to warfarin’s significant interindividual variability in pharmacokinetics and pharmacodynamics, its rate of adverse events is among the highest of all commonly prescribed drugs. However, with the discovery of polymorphic variants in CYP2C9 and VKORC1 genotypes, two genes that encode the individual anticoagulant response, physicians can use patients’ gene profile to prescribe optimum doses of warfarin to prevent side effects such as major bleeding and to allow sooner and better therapeutic efficacy. The pharmacogenomic process for discovery of genetic variants that predict adverse events to a specific drug has been termed toxgnostics.

Drug development and usage
Last paragraph.

New methods are needed for delivering personalized drugs generated from pharmacy compounding efficiently to the disease sites of the body. For instance, researchers are now trying to engineer nanocarriers that can precisely target the specific site by using real-time imaging and analyzing the pharmacodynamics of the drug delivery. Currently, several candidate nanocarriers are being investigated, which are Iron oxide nanoparticles, quantum dots, carbon nanotubes, gold nanoparticles, and silica nanoparticles. Alteration of surface chemistry allows these nanoparticles to be loaded with drugs, as well as to avoid the body’s immune response, making nanoparticle-based theranostics possible. Nanocarriers’ targeting strategies are varied according to the disease. For example, if the disease is cancer, a common approach is to identify the biomarker expressed on the surface of cancer cells and to load its associated targeting vector onto nanocarrier to achieve recognition and binding; the size scale of the nanocarriers will also be engineered to reach the enhanced permeability and retention effect (EPR) in tumor targeting. If the disease is localized in the specific organ, such as the kidney, the surface of the nanocarriers can be coated with a certain ligand that binds to the receptors inside that organ to achieve organ-targeting drug delivery and avoid non-specific uptake. Despite the great potential of this nanoparticle-based drug delivery system, the significant progress in the field is yet to be made, and the nanocarriers are still being investigated and modified to meet clinical standards.

Respiratory proteomics
In specific, proteomics is used to analyze a series of protein expressions, instead of a single biomarker. Proteins control the body’s biological activities including health and disease, so proteomics is helpful in early diagnosis. In the case of respiratory disease, proteomics analyzes several biological samples including serum, blood cells, bronchoalveolar lavage fluids (BAL), nasal lavage fluids (NLF), sputum, among others. The identification and quantification of complete protein expression from these biological samples are conducted by mass spectrometry and advanced analytical techniques. Respiratory proteomics has made significant progress in the development of personalized medicine for supporting health care in recent years. For example, in a study conducted by Lazzari et al. in 2012, the proteomics-based approach has made substantial improvement in identifying multiple biomarkers of lung cancer that can be used in tailoring personalized treatments for individual patients. More and more studies have demonstrated the usefulness of proteomics to provide targeted therapies for respiratory disease.

Population screening
A lot of molecular-scale information about patients can be easily obtained through the use of genomics (microarray), proteomics (tissue array), and imaging (fMRI, micro-CT) technologies. These so-called molecular biomarkers such as genetic mutations have proven to be very powerful in disease prognosis, such as cancer prognosis. The main three areas of cancer prediction fall under cancer recurrence, cancer susceptibility and cancer survivability. When the molecular scale information is combined with macro-scale clinical data such as patients’ tumor type and other risk factors, the prognosis is significantly improved. Consequently, given the use of molecular biomarkers, especially genomics, cancer prognosis or prediction has become very effective, especially when screening a large population. Essentially, population genomics screening can be used to...

Challenges
As personalized medicine is practiced more widely, a number of challenges arise. The current approaches to intellectual property rights, reimbursement policies, patient privacy, data biases and confidentiality as well as regulatory oversight will have to be redefined and restructured to accommodate the changes personalised medicine will bring to healthcare. Furthermore, the analysis of acquired diagnostic data is a recent challenge of personalized medicine and its implementation..., which requires the interdisciplinary cooperation of experts from specific fields of research, such as medicine, clinical oncology, biology, and artificial intelligence.

Regulatory oversight
A major challenge for those regulating personalized medicine is a way to demonstrate its effectiveness relative to the current standard of care. The new technology must be assessed for both clinical and cost effectiveness, and as it stands, regulatory agencies have no standardized method.

Patient privacy and confidentiality
... have to be considered.

Moreover, we could refer to the privacy issue at all layers of personalized medicine from discovery to treatment. One of the leading issues is the consent of the patients to have their information used in genetic testing algorithms primarily AI algorithms. The consent of the institution who is providing the data to be used is of prominent concern as well. In 2008,...

Data Biases
Data biases also play an integral role in personalized medicine. It is important to ensure that the sample of genes being tested come from different populations. This is to ensure that the samples do not exhibit the same human biases we use in decision making.

Consequently, if the designed algorithms for personalized medicine are biased, then the outcome of the algorithm will also be biased because of the lack of genetic testing in certain populations. For instance, the results from the Framingham Heart Study have led to biased outcomes of predicting the risk of cardiovascular disease. This is because the sample was tested only on white people and when applied to the non-white population, the results were biased with overestimation and underestimation risks of cardiovascular disease.

Implementation
Aside from issues related to the healthcare system, there are still several issues that must be addressed before personalized medicine can be implemented. Currently, very little of the human genome has been analyzed, and even if healthcare providers had access to a patient’s full genetic information, very little of it could be effectively leveraged into treatment. Challenges also arise when processing such large amounts of genetic data. Even with error rates as low as 1 per 100 kb, processing a human genome could have roughly 30,000 errors. This many errors, especially when trying to identify specific markers, can make discoveries, as well as verifiability difficult. There are methods to overcome this, but as it stands, they are computationally taxing, as well as expensive. There are also issues from an effectiveness standpoint, as after the genome has been processed, function in the variations among genomes must be analyzed using GWAS s. While the impact of the SNPs discovered in these kinds of studies can be predicted, more work must be done to control for the vast amounts of variation that can occur because of the size of the genome being studied. In order to effectively move forward in this area, steps must be taken to ensure the data being analyzed is good, and a wider view must be taken in terms of analyzing multiple SNPs for a phenotype. The most pressing issue that the implementation of personalized medicine is to apply the results of genetic mapping to improve the healthcare system. This is not only due to the infrastructure and technology required for a centralized database of genome data, but also the physicians that would have access to these tools would likely be unable to fully take advantage of them. In order to truly implement a personalized medicine healthcare system, there must be an end-to-end change.