Master of Engineering in Pharmaceutical Engineering


The Master of Engineering in Pharmaceutical Engineering Program is currently under review and revision by the College of Engineering. It is currently closed to new students. If you would like to be notified in the event that the application is re-opened, please complete this inquiry form.

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e-Clinical Science Concentration

Program Overview

Explosive advances in new genomic and biomedical sciences are unraveling various disease processes and creating new directions for novel drug research and development. The magnitude of the potential applications of these advances is staggering since more than 100 million Americans suffer from diseases such as heart disease, cancer, Alzheimer's, diabetes, arthritis, clinical depression, stroke, osteoporosis and others, costing society well over $550 billion annually.

Drug and medical device companies are striving to turn these scientific discoveries into actual products. In recent years, R&D expenditures for PhRMA member companies have ballooned to over $34B while the number of New Chemical Entities (NCEs) approved has inched down. Reasons cited for poor productivity include rapidly rising costs and the increasing complexity of the R&D process in an environment characterized by a tremendous increase in the number of targets identified through advances in genomics.

There is also a need to turn the process of developing new products from an art to a well-understood science. One key bottleneck is the design and execution of clinical trials to prove safety and efficacy of innovative biomedical products while balancing heightened regulatory pressures with an unrelenting drive to control rising costs and improve quality.

It is widely believed that the adoption and use of information technology is critical to companies working to satisfy these demands within the drug-development operating environment. "E-clinical" science, engineering, and technologies can reduce data collection and management inefficiencies, improve development planning and management, and accelerate the filing of drugs with regulatory agencies.

For example, electronic data capture using web-enabled and voice-response technologies can be used to replace paper data capture. Data collection and management activities account for more than 60 percent of the total time within which Phase I-III programs are conducted. Experts estimate that the global pharmaceutical industry could achieve substantial savings and reductions in cycle time by implementing electronic data capture and management technologies.

Additionally, the use of Clinical Trial Management Systems (CTMS) and Project and Portfolio Management (PPM) Systems can streamline and optimize planning, resource management, and trial execution processes akin to automated operations deployed by the process and manufacturing industry. Web-based technologies and collaboration platforms can be used to facilitate interaction between globally dispersed participants in the clinical trials process including sponsors, clinical investigators, CROs, central labs, IRBs and regulatory bodies. Finally, a variety of technology-based approaches can be utilized to optimize the patient recruitment and retention process that has also emerged as a key bottleneck.

The Status of e-Clinical Trials

We need medical scientists, bioengineers, and managers with a comprehensive knowledge of regulatory affairs, dosage formulation, and clinical issues as well as skills such as scientific computing, database and information management, and automation. This background is becoming essential for drug design and pre-clinical and clinical development of traditional and atypical pharmaceuticals as well as novel biomedical devices and combination products. Potential students should also be familiar with the rapid changes in social, regulatory, legal and business aspects in addition to the technical issues mentioned above.

Degree Requirements

The Master of Engineering in Pharmaceutical Engineering requires a total of 30 credit hours of course work, of which at least 24 credit hours must be graded (not satisfactory or unsatisfactory), and at least 18 graded credit hours must be in courses at the 500 level and above. A minimum grade point average of 5.0/9.0 (i.e., a "B" average) is also required. Each entering student will be assigned to a faculty advisor and must obtain the advisor's approval for the overall program of study and semester courses. Each student is allowed to take up to 3 credit hours on an assigned clinical research project to satisfy the degree requirement. Besides several required core courses (ChE/Pharm 519 and 597), the student can elect the remaining courses according to individual interest within the clinical development and engineering concentration framework as described in the sample schedule.

Prerequisites

  • Bachelor's degree in engineering or related science discipline.
  • At least two years of college engineering mathematics, or equivalent.
  • Undergraduate coursework in the following areas: human physiology; statistics and matrix algebra (Math 217 or equivalent). Students without such background must take remedial courses before embarking on the degree program.
  • The equivalence of two years of full-time industrial experience in pharmaceutical and related industries. Students with outstanding qualifications who do not have two years of industrial experience can be considered for admission if they have relevant summer internship or co-op experience.
  • The Graduate Record Examination (GRE) is required for all students who have not previously earned a degree from the University of Michigan. (Students seeking financial aid must submit GRE scores).

Sample Schedule

Incoming students must obtain approval from their faculty advisor for the planned M. Eng. in Pharmaceutical Engineering degree courses selected. A faculty advisor will be assigned to the student upon admission. Up to 6 credit hours of research based on a capstone project that utilize the student's knowledge and apply it to an industrially relevant problem. The project must receive prior approval by the student's advisor.

Course offerings are subject to change. Check university course listings for details.

1. Pharmaceutical Engineering Core Courses (15 credit hours)
Course NumberCreditsTermTitle
ChE/Pharm 519 3 F Modern Pharmaceutical Engineering
ChE/Pharm 596 1 TBD Health Science and Engineering Seminar Series
ChE/Pharm 597 2 F Regulatory Science for Scientists, Engineers and Managers
ChE 520 2 alt. W Applied Pharmacokinetics and Toxicokinetics
HMP 652 3 F Health Law
IOE 461 3 W Quality Engineering
Stat 465 3 W Design of Experiments
Biostat 619 2 F Clinical Trials
2. Advanced Pharm/Engineering and Statistics/Biostatistics Core Courses
(at least 6 credit hours)
Each student must take at least one advanced science/engineering course and one advanced statistics or biostatistics course from the following list:
Course NumberCreditsTermTitle
Math 417 3 F/W Matrix Algebra (if no linear algebra taken before)
BME 401 4 F The Human Body: Its Structure and Function
BME 516 3 F Medical Imaging Systems
Pharm 760 2 W Advanced Pharmacokinetics and Biopharmaceutics
Pharmcol. 611 2 W Principles of Pharmacology
EHS 506 2 F Principles of Toxicology
BME 550 1 F Ethics and Enterprise
EHS 508 2 W Principles of Risk Assessment
EECS 484 4 F/W Database Management System
EECS 485 4 W Web Database and Information System
Biostat 675 3 F Survival Time Analysis
Biostat 875 3 W Advanced Topics in Survival Analysis
Bioinf 526 3 F Fundamentals of Bioinformatics
ChE 696 3 alt W Molecular Systems Biology

Other advanced engineering and statistics/biostatistics courses may be used. The students should check with their advisor and Program Director for approval before taking these courses.

3. Concentration Core Courses (at least 6 credit hours)
Course NumberCreditsTermTitle
HMP 668 3 W Health Informatics
Biostat 558 3 W Clinical Trials Experimental Design
PSYCH 449 3 F Decision Processes
Pharm 761 2 alt W Population Pharmacokinetics
Stat 503 3 F Applied Multivariate Analysis
Biostat 690 3 W Health Application of Multivariate Analysis