Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments

Validating the accuracy of mathematical model-based pharmacogenomics dose prediction with real-world data

OBJECTIVE

The study aims to verify the usage of mathematical modeling in predicting patients' medication doses in association with their genotypes versus real-world data.

METHODS

The work relied on collecting, extracting, and using real-world data on dosing and patients' genotypes. Drug metabolizing enzymes, i.e., cytochrome CYP 450, were the focus. A total number of 1914 subjects from 26 studies were considered, and CYP2D6 and CYP2C19 gene polymorphisms were used for the verification.

RESULTS

Results show that the mathematical model was able to predict the reported optimal dosing of the values provided in the considered studies. Predicting patients' optimal doses circumvents trial and error in patients' treatments.

DISCUSSION

The authors discussed the advantages of using a mathematical model in patients' dosing and identified multiple issues that would hinder the usability of raw data in the future, especially in the era of artificial intelligence (AI). The authors recommend that researchers and healthcare professionals use simple descriptive metabolic activity terms for patients and use allele activity scores for drug dosing rather than phenotype/genotype classifications.

CONCLUSION

The authors verified that a mathematical model could assist in providing data for better-informed decision-making in clinical settings and drug research and development.

Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal

In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to selective serotonin reuptake inhibitors (SSRIs) antidepressants in major depressive disorder (MDD) patients prior to the treatment using EEG signal. The effective connectivity of 30 MDD patients was determined by analyzing their pretreatment EEG signals, which were then concatenated into delta, theta, alpha, and beta bands and transformed into images. Using these images, we then fine tuned a hybrid Convolutional Neural Network that is enhanced with bidirectional Long Short-Term Memory cells based on transfer learning. The Inception-v3, ResNet18, DenseNet121, and EfficientNet-B0 models are implemented as base models. Finally, the models are followed by BiLSTM and dense layers in order to classify responders and non-responders to SSRI treatment. Results showed that the EfficiencyNet-B0 has the highest accuracy of 98.33, followed by DensNet121, ResNet18 and Inception-v3. Therefore, a new method was proposed in this study that uses deep learning models to extract both spatial and temporal features automatically, which will improve classification results. The proposed method provides accurate identification of MDD patients who are responding, thereby reducing the cost of medical facilities and patient care.

Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders

Metabolic disorders are increasingly prevalent worldwide, leading to high rates of morbidity and mortality. The variety of metabolic illnesses can be addressed through personalized medicine. The goal of personalized medicine is to give doctors the ability to anticipate the best course of treatment for patients with metabolic problems. By analyzing a patient's metabolomic, proteomic, genetic profile, and clinical data, physicians can identify relevant diagnostic, and predictive biomarkers and develop treatment plans and therapy for acute and chronic metabolic diseases. To achieve this goal, real-time modeling of clinical data and multiple omics is essential to pinpoint underlying biological mechanisms, risk factors, and possibly useful data to promote early diagnosis and prevention of complex diseases. Incorporating cutting-edge technologies like artificial intelligence and machine learning is crucial for consolidating diverse forms of data, examining multiple variables, establishing databases of clinical indicators to aid decision-making, and formulating ethical protocols to address concerns. This review article aims to explore the potential of personalized medicine utilizing omics approaches for the treatment of metabolic disorders. It focuses on the recent advancements in genomics, epigenomics, proteomics, metabolomics, and nutrigenomics, emphasizing their role in revolutionizing personalized medicine.

Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology

More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.

Artificial Intelligence in the Clinical Laboratory: An Overview with Frequently Asked Questions

This article provides an overview of machine learning fundamentals and some applications of machine learning to clinical laboratory diagnostics and patient management. A key goal of this article is to provide a basic foundation in clinical machine learning for readers with clinical laboratory experience that will set them up for more in-depth study of the topic and/or to become a better collaborator with computational colleagues in the development and deployment of machine learning-based solutions.

Evaluation of supervised machine learning algorithms in predicting the poor anticoagulation control and stable weekly doses of warfarin

BACKGROUND

Machine learning algorithms (MLAs) carry a huge potential in identifying predicting factors and are being explored for their utility in the field of personalized medicine.

AIM

We aimed to investigate MLAs for identifying predictors (clinical and genetic) of poor anticoagulation status (ACS) and stable weekly warfarin dose (SWWD).

METHOD

Clinical factors, in addition to the CYP2C9, VKORC1, and CYP4F2 genotypes, were obtained for patients receiving warfarin for at least the previous six months. The C5.0 decision tree classification algorithm was used to predict poor ACS while classification and regression tree analysis (CART), in addition to the Chi-square automatic interaction detector (CHAID), was used to predict SWWD. The percentage of patients within 20% of the actual dose, root mean squared error (RMSE), and area under the receiver-operating characteristics curve (AUROC) were identified as performance indicators of the models.

RESULTS

In the C5.0 classification decision tree, the CYP4F2 genotype was the strongest predictor of ACS (AUROC = 0.53). In the CART analysis of SWWD, VKORC1 polymorphisms were the most significant predictor, followed by the CYP2C9 genotype (percentage of patients within 20% of the actual dose = 38.2%, RMSE = 13.6). For the CHAID algorithm, the percentage of patients within 20% of the actual dose was 49%, while the RMSE was found to be 13.4.

CONCLUSION

Genetic and non-genetic predictive factors were identified by the MLAs for ACS and SWWD. Further, the need to externally validate the MLAs in a prospective study was highlighted.