Course Description: 

Review principles and research methodologies of the complete new drug discovery and development in preclinical studies including in-depth pharmacological research in both in vitro and in vivo studies; modeling design; ethical consideration, and international regulations

Learning Outcome(s) :  –

Course Description: 

All essential principles of clinical drug development: applied clinical pharmacology for first-in-human and early phase trials; study designs of clinical trial phase I, II, and III; ethical and international regulations for new drug approval; post-marketing studies including pharmacoepidemiology, pharmacoeconomics, and drug cycle.

Learning Outcome(s) :  –

Course Description: 

Advancement of various aspects of biopharmaceutical research and industry: gene editing technologies (such as CRISPR/Cas9), growth in immuno–oncology, targeting ribonucleic acid (RNA); an increasing focus on human microbiome; growing excitement about artificial intelligence (AI) for drug discovery/development; novel antibiotics for the threat of bacterial resistance; a controversial rapid growth of medical cannabis.

Learning Outcome(s) :  –

Course Description: 

Basic pharmacology, toxicology, biostatistics, and pharmacogenomics; regulatory science, the process of the preclinical and clinical drug development.

Learning Outcome(s) :  –

Course Description: 

Mechanisms of drugs interacting with systems of the human body; the components of molecule, enzyme, and pathway involving each system in the human body; the identified possible targets of diseases for the current treatment and the future treatment.

Learning Outcome(s) :  –

Course Description: 

The system-level reasoning of complex biological processes; advanced omics techniques for the system pharmacology research; basic handling of big data from omic measurement; computational concepts in the biomolecular dynamics, signaling cascades, feedback regulations and biological noises.

Learning Outcome(s) :  –

Course Description: 

Hands-on skills on data normalization and preprocessing; advanced computational principles for analyzing and visualizing complex high-throughput omics data from genomics, transcriptomics, and antibody-based proteomics; the dimensional reduction; network analysis; data clustering; analysis and management of clinical data.

Learning Outcome(s) :  –

Course Description: 

Free radicals and reactive oxygen species (ROS) production; antioxidant defense systems; redox signaling pathway mechanisms; oxidative stresses and their effects in aging, cancer, metabolic diseases and other pathological conditions; ATP production mechanisms of the cell; mitochondrial dysfunctions in diseases; current advanced researches and therapeutic applications in redox and bioenergetic pharmacology.

Learning Outcome(s) :  –

Course Description: 

Oncogene addiction; Synthetic lethality; Intra–tumor heterogeneity; Immune checkpoint blockade; Neoantigen prediction and cancer vaccine; Acquired vulnerability; Genetic and non–genetic resistance mechanisms; Molecular–based and tumor–agnostic biomarkers; Pan–omic subgrouping approaches; Cancer immunotherapy biomarkers; Umbrella and basket trials; Genomic tumor board.

Learning Outcome(s) :  –

Course Description: 

MATLAB, Tellurium and R languages: implement functions, solve ordinary differential equations, build tellurium-based dynamical models, and develop regression models.

Learning Outcome(s) :  

At the end of the course students will be able to
1. Explain the principles of computing tools in Matlab, tellurium and R languages, creating basic differential equation solving functions, dynamic modeling with Tellurium and model development regression for the study of the relationship of variables for research in systemic pharmacology and can apply the knowledge gained to solve problems
2. Processes basic large-scale omics data, using Matlab, Tellurium, and R languages, we create basic differential equation solving functions. dynamic modeling with Tellurium and model development regression for the study of the relationship of variables for research in systemic pharmacology
3. Plan and design a pharmacological study based on knowledge, methods and research processes for new drug development. at the level of basic pharmacology both at the laboratory level and in the use of laboratory animals Design, modeling, ethical considerations and benchmarks
4. Demonstration of tool selection in pharmacological calculations communication and information technology appropriately

Course Description: 

R and Python languages: pre-process large dataset, visualize big data, process different omic databases, Implement quantitative image processing, machine-learning tools for data mining.

Learning Outcome(s) :  –

Course Description: 

Application of updated research methodology in pharmacology and new drug development; transcriptome and proteome profiling technologies; updated techniques for active principle analysis; updated techniques for protein and nucleic acid analysis; updated techniques in metabolomics and pharmacokinetics; updated imaging techniques in pharmacology; updated techniques for genetic perturbation; the updated animal research in pharmacology and new drug development.

Learning Outcome(s) :  –

Course Description: 

Data, database, bioinformatic tools; application for genetic materials analysis, proteins, biomolecules, and chemicals.

Learning Outcome(s) :  –

Course Description: 

Concepts of precision medicine, stratifying patients using next-generation sequencing data and clinical data, applying other ‘omic’ methodology to complement genomic blind plots for identifying new diagnostic and prognostic markers, case studies in tumor profiling, using blood-based gene expression profiles in cancer prognosis, building and visualizing omic data, issues on patient privacy, ethical challenges in precision medicine.

Learning Outcome(s) :  –

Course Description: 

Introduction to advanced flow cytometry; flow cytometric operation; high dimensional data analysis; flow cytometric experimental design; advanced flow cytometry techniques for research; quality control in flow cytometry; flow cytometric data presentation; specific cell subset isolation by flow cytometric-based cell sorting; recent advance in flow cytometry.

Learning Outcome(s) :  –