Study content

Our curriculum is designed to address all critical aspects of the industry and equip you to take on a variety of roles and leadership positions.

These subjects provide a comprehensive knowledge of the most important areas of the pharmaceutical and healthcare industry. The Pharma Masterclass programme not only opens up new career opportunities for you, but also gives you a competitive edge in the job market.

Study Content:

Business Development, Intellectual Property Protection, Life-cycle management

Semmelweis University, Faculty of Medicine
Pharmaceutical Innovation and Business Administration Master of Science

Name of the host institution (and any contributing institution): Department of Pharmacology and Pharmacotherapy

Name of subject: Business Development, Intellectual Property Protection, Life-cycle management

English: Business Development, Intellectual Property Protection, Life-cycle management

in German: not applicable

Credit value: 5

Semester: 2025/2026 2nd Semester

Hours per semester	Lecture	Course work	Seminar
150	25	125	–

Hours per week	Lecture	Practical lesson	Seminar
Course blocks tailored to the students’ employment obligations on Fridays and Saturdays

Type of course:

compulsory

Academic year:

2025/2026

Language of instruction (for optional and elective subjects):

English

Course code:

(in the case of a new course, to be completed by the Dean’s Office, following approval)

Course coordinator name:

Prof. Dr. Péter Ferdinandy

Course coordinator location of work, telephone availability:

Semmelweis University Department of Pharmacology and Pharmacotherapy, 1089 Budapest, Nagyvárad tér 4. Tel: +36-1-2104416, e-mail: ferdinandy.peter@semmelweis.hu

Course coordinator position:

Head of Department, full professor

Course coordinator Date and number of habilitation:

June 2 2001., 26/2001 Hab.

Objective of instruction and its place in the curriculum:

This course introduces participants to the fundamentals of intellectual property protection, business development, strategic planning, and product life-cycle management in the pharmaceutical and biotech industry. Learners will gain practical skills to analyze markets, evaluate opportunities, and develop actionable business plans for new and existing products. The course also covers strategies for
partnerships, licensing, and collaborations, as well as approaches to maximize product value and navigate challenges throughout its life cycle.

Method of instruction (lecture, group work, practical lesson, etc.):

Lecture, course- -work

Competencies acquired through completion of course:

…….

Course outcome (names and codes of related subjects):

…….

Prerequisites for course registration and completion: (CODE):

None

In the case of multi-semester courses, position on the possibility of and conditions for concurrent registration:

None

The number of students required to start the course (minimum, maximum), student selection method:

All students admitted to the MSc course

Detailed course syllabus (if the course can be divided into modules, please indicate):

(Theoretical and practical instruction must be broken down into hours (weeks), numbered separately; names of instructors and lecturers must be listed, indicating guest lecturers/instructors. It cannot be attached separately! For guest lecturers, attachment of CV is required in all cases!)

Basics of Intellectual Property Protection for Business – Renata Papp MD PhD – 4*45’

Market Analysis and Opportunity Assessment (Market research fundamentals, SWOT Analysis and Competitive Positioning, Evaluating product opportunities, Basics of value proposition and targeting) – David N Bernstein Visiting Professor – 4*45’

Business Planning and Strategy (Introduction to Business Plans in Pharma/Biotech, Strategic Planning Basics, Partnerships and Collaborations, Simple Financial Planning, Risk Assessment and Mitigation) – David N Bernstein Visiting Professor – 6*45’

Business Case Workshop – Hands-on exercise: develop a mini business case for a product 4*45’

Case Study: Real-World Product Strategy 5*45’

Raising Capital – 2*45’

Other courses with overlapping topics (obligatory, optional, or elective courses) in interdisciplinary areas. To minimalize overlaps, topics should be coordinated. Code(s) of courses (to be provided):

None

Requirements for attendance, options for making up missed sessions, and method of absence justification:

Full attendance is required. Completing additional e-learning materials are required to make up missed courses. Assessment methods during semester (number, topics, and dates of midterms and reports, method of inclusion in the course grade, opportunities for makeup and improvement of marks):

(number, topics, and dates of midterms and reports, method of inclusion in the course grade, opportunities for make-up and improvement of marks)

Project work and online test at the end of the semester

Number and type of individual assignments to be completed, submission
deadlines:

Project work focused on a given topic, after the end of lectures

Requirements for the successful completion of the course:

Project work approved + appropriate test results

Type of assessment:

score-based

Examination requirements (list of examination topics, subject areas of tests, lists of mandatory parameters, figures, concepts and calculations, practical skills, optional topics for the project assignment recognized as an exam and the criteria for its completion and evaluation)

project work submitted – test completed. Test includes questions regarding all topics of the subject.

Method and type of grading (Share of theoretical and practical examinations in the overall evaluation. Inclusion of the results in the end-of-term assessment. Possibilities of and conditions for offered grades.): (Share of theoretical and practical examinations in the overall evaluation, Inclusion of the results in the end-of-term assessment, Possibilities of and conditions for offered grades)

score-based evaluation of the test results. Assessment of the project work: whether it reached at least the satisfactory level.

Formulation of the grade:

88 to 100 points: excellent (5)
76-87,5 points: good (4)
63-75 points: average (3)
50-62 points: satisfactory (2)
Less than 50 points: unsatisfactory (1)

Signature of habilitated instructor (course coordinator) announcing the course:

Prof. Dr. Péter Ferdinandy

Signature of the director of the host institution:

Prof. Dr. Péter Ferdinandy

Date of submission:

5th January 2026

Clinical Phase of Drug Development

Semmelweis University, Faculty of Medicine
Pharmaceutical Innovation and Business Administration Master of Science

Name of the host institution (and any contributing institution): Department of Pharmacology and Pharmacotherapy

Name of subject: Clinical Phase of Drug Development

in English: Clinical Phase of Drug Development

in German: not applicable

Credit value: 5

Semester: 2025/2026 2nd Semester

Hours per semester	Lecture	Course work	Seminar
	16		6

Hours per week	Lecture	Practical lesson	Seminar
Course blocks tailored to the students’ employment obligations on Fridays and Saturdays

Type of course:

compulsory

Academic year:

2025/2026

Language of instruction (for optional and elective subjects):

English

Course code:

(in the case of a new course, to be completed by the Dean’s Office, following approval)

Course coordinator name:

Prof. Dr. István Bitter

Course coordinator location of work, telephone availability:

Semmelweis University, Department of Psychiatry and Psychotherapy

Course coordinator position:

Professor

Course coordinator Date and number of habilitation:

Objective of instruction and its place in the curriculum:

To equip students with the knowledge and practical skills needed to plan, and manage clinical trials in compliance with regulatory, ethical, and industry standards.

Understanding the management of clinical trials is crucial during drug development because clinical trials are the backbone of demonstrating a drug’s safety, efficacy, and quality before it reaches patients. Proper management ensures that trials are conducted efficiently, ethically, and in compliance with regulatory standards, which directly impacts the success of the drug development process.

Method of instruction (lecture, group work, practical lesson, etc.):

pre-recorded online lectures
real-time online lectures
in-person seminar

Competencies acquired through completion of course:

By the end of the course, students will be able to:

Design and conduct trials in compliance with regulatory and ethical standards.
Manage clinical trials to ensure safe, ethical, and efficient drug development.
Ensure high-quality, reliable, and accurate data for decision-making.
Coordinate effectively with investigators and sponsors (and their representatives).
Identify and mitigate operational and clinical risks.
Support timely decisions in drug development.
Prevent delays in the management of trials and regulatory setbacks.
Contribute to bringing safe and effective treatments to patients efficiently.

Course outcome (names and codes of related subjects):

none

Prerequisites for course registration and completion: (CODE):

none

In the case of multi-semester courses, position on the possibility of and conditions for concurrent registration:

none

The number of students required to start the course (minimum, maximum), student selection method:

all students admitted

Detailed course syllabus (if the course can be divided into modules, please indicate):

Introduction: objectives of the course; project: planning a phase II or III clinical trial – 1*45’- Prof. Dr. István Bitter

Manage transition from translational medicine to clinical development phase (Phase 0-I) – Prof. Dr. Kerpel-Fronius Sándor – 2*45’

Pharmacokinetics for Drug Development: Planning and Analysis – Dr. Tóthfalusi László – 1*45’

Protocol of a clinical trial: structure; obligatory parts – Prof. Dr. Peter Arányi – 2*45’

Investigators brochure – Prof. Dr. Peter Arányi – 1*45’

Clinical Study Design and Protocol Development – Prof. Dr. István Bitter – 2*45’ (hypotheses; primary and other endpoints; blinding; control (active or placebo; length of the study)

Developing the statistical analysis plan of a protocol (including effect size and sample size estimations; presenting efficacy and safety data) Dr. Czobor Pál – 2*45’

Summary of the Product Characteristics document through the lifecycle of a drug- Dr. Ágota Barabássy – 1*45’

Project consultation (with all participants present): Dr. Pál Czobor and Prof. Istvan Bitter 2*45’

Complex organization of clinical trials. Duties and responsibilities of the Sponsor and investigator/s.– Dr. Veres László – 1*45’

Planning and controlling of the monitoring of clinical trials – Dr. Veres László – 1*45’

Using Real-World Evidence in Drug Development – Dr. Barótfi Szabolcs – 1*45’

Regulatory review of Phase II-IV clinical trials – Dr. Ágnes Hajdú – 1*45’

Presenting the projects and oral exam 4*45’ – Prof. Dr. Istvan Bitter (presentation the participation of all students) (in person)

None

Requirements for attendance, options for making up missed sessions, and method of absence justification:

Full attendance is required. Completing additional e-learning materials are required to make up missed courses.

Assessment methods during semester (number, topics, and dates of midterms and reports, method of inclusion in the course grade, opportunities for make-up and improvement of marks):

(number, topics, and dates of midterms and reports, method of inclusion in the course grade, opportunities for make-up and improvement of marks)

Project work oral-presentation and defense

Number and type of individual assignments to be completed, submission deadlines:

Oral presentation at the end of the semester

Requirements for the successful completion of the course:

Project work oral defense

Project work submitted

Formulation of the grade:

88 to 100 points: excellent (5)
76-87,5 points: good (4)
63-75 points: average (3)
50-62 points: satisfactory (2)
Less than 50 points: unsatisfactory (1)

Signature of habilitated instructor (course coordinator) announcing the course:

Prof. Dr. István Bitter

Signature of the director of the host institution:

Prof. Dr. Péter Ferdinandy

Date of submission:

22nd December 2025

Critical literature reading

Semmelweis University, Faculty of Medicine
Pharmaceutical Innovation and Business Adminstration Master of Science

Name of the host institution (and any contributing institution): Department of Pharmacology and Pharmacotherapy hosting the MSc course announcing this specific subject in collaboration with the Centre for Translational Medicine

Name of subject: Critical literature reading

in English: Critical literature reading

in German: not applicable

Credit value: 5

Semester: 2025/2026 1st Semester

in which the subject is taught according to the curriculum

Hours per semester	Lecture	Course work	Seminar
150	25	125

Hours per week	Lecture	Course work	Seminar
Course blocks tailored to the students’ employment obligations. Course dates: 10 Oct 14.00-18.00 6 Nov 08:00-16:00 28 Nov 14.00-18.00

Type of course:

compulsory

Academic year:

2025/2026

Language of instruction (for optional and elective subjects):

English

Course code:

new course
(in the case of a new course, to be completed by the Dean’s Office, following approval

Course coordinator name:

Prof. Dr. Péter Hegyi

Course coordinator location of work, telephone availability:

Semmelweis University, Centre for Translational Medicine, +36-30/0164407

Course coordinator position:

Professor

Course coordinator Date and number of habilitation:

2011

Objective of instruction and its place in the curriculum:

The objective of instruction is to develop students’ ability to critically assess scientific publications and engage with current research in a structured and analytical manner. The course lays a foundation for evidence-based thinking and supports the development of research competencies essential for advanced academic work and thesis preparation.

Method of instruction (lecture, group work, practical lesson, etc.):

Lectures, group work, home-works, and e-learning.

Competencies acquired through completion of course:

Through completion of the course, students will acquire the ability to critically evaluate scientific literature, interpret research findings, and identify strengths and limitations in published work. They will also develop skills in evidence-based reasoning, academic collaboration, and independent learning.

Course outcome (names and codes of related subjects):

none

Prerequisites for course registration and completion: (CODE):

none

In the case of multi-semester courses, position on the possibility of and conditions for concurrent registration:

none

The number of students required to start the course (minimum, maximum), student selection method:

all students admitted

Detailed course syllabus (if the course can be divided into modules, please indicate):

Course topics include:

Introduction to evidence-based research and the role of systematic reviews
Formulating research questions using the PICO framework
Literature search strategies and database use (e.g., PubMed, Cochrane Library)
Study selection, inclusion/exclusion criteria, and data extraction
Assessing the quality and risk of bias in individual studies
Introduction to meta-analysis: concepts, effect measures, and heterogeneity
Strengths and limitations of systematic reviews and meta-analyses
Common sources of bias and how to identify them
Reporting standards (e.g., PRISMA) and transparency in methodology
Critical appraisal of published systematic reviews and meta-analyses

Course structure:

Lectures: Theoretical background on methodology and key concepts

E-learning: Online video modules to support self-paced learning

Practical sessions: Group and individual work focused on real-world examples

None

Requirements for attendance, options for making up missed sessions, and method of absence justification:

Full attendance is required. Completing additional e-learning materials are required to make up missed courses.

Assessment methods during semester (number, topics, and dates of midterms and reports, method of inclusion in the course grade, opportunities for make-up and improvement of marks):

(number, topics, and dates of midterms and reports, method of inclusion in the course grade, opportunities for make-up and improvement of marks)

Attendance, group work activity.

Number and type of individual assignments to be completed, submission deadlines:

January 26, 2026

Requirements for the successful completion of the course:

Attendance and passed project work.

Type of assessment:

Project work

All materials will be provided during the course

Passed/ Failed – practical exam based on the project work

Signature of habilitated instructor (course coordinator) announcing the course:

Prof. Dr. Péter Hegyi

Signature of the director of the host institution:

Prof. Dr. Péter Hegyi

Date of submission:

8th August 2025

Data management and statistics

Semmelweis University, Faculty of Medicine
Pharmaceutical Innovation and Business Administration Master of Science

Name of the host institution (and any contributing institution): Department of Pharmacology and Pharmacotherapy in collaboration with the Institute of Biostatistics and Network Science of Semmelweis University

Name of subject: Data management and statistics

in English: Data management and statistics

in German: Not applicable

Credit value: 5

Semester: 2025/2026 1st Semester

in which the subject is taught according to the curriculum

Hours per semester	Lecture	Course work	Consultation
150	15	130	5

Hours per week	Lecture	Course work	Consultation
Course blocks tailored to the students’ employment obligations on Fridays and Saturdays Course dates: 4th October 9.00-12.00 17th October 9.00-12.00 24th October 14.00-18.00 7th November 14.00-18.00 13rd December 9.00-12.00

Type of course:

compulsory

Academic year:

2025/2026

Language of instruction (for optional and elective subjects):

English

Course code:

(in the case of a new course, to be completed by the Dean’s Office, following approval)

Course coordinator name:

Dr. Roland Molontay

Course coordinator location of work, telephone availability:

Semmelweis University, 1082 Budapest, Baross u. 22 tel.: +36205913228

Course coordinator position:

Director

Course coordinator Date and number of habilitation:

Objective of instruction and its place in the curriculum:

The aim of the course is to equip students with the skills to recognize business and operational challenges within the pharmaceutical and healthcare sectors where data analysis and data science can provide strategic value. Through practical prototyping, students will learn to demonstrate and communicate the competitive advantages of data-driven solutions. The course covers both theoretical and practical foundations of data analysis methods relevant to economic and managerial decision-making in the life sciences industry. Students will acquire quantitative tools for analyzing and predicting industry-relevant phenomena. Beyond delivering essential theoretical knowledge, the course emphasizes practical problem-solving, real-world case studies, and the cultivation of a data-oriented mindset tailored to the pharmaceutical business environment.

Method of instruction (lecture, group work, practical lesson, etc.):

real-time online lectures

Competencies acquired through completion of course:

Understands the key tasks of business data analysis, the main areas of expertise, and the tools applicable in each.
Understands the technical details of the main steps in data analysis: data collection, data preparation, modeling, evaluation, and application.
Has knowledge of the most important theoretical models and algorithms in data science, including the basic paradigms of supervised and unsupervised machine learning.
Knows the fundamental tools and methods of data visualization.
Is familiar with the basic operation of data-driven decision support tools.
Understands the most important micro- and macroeconomic applications of data science, data analysis, and data visualization, particularly in the field of business intelligence.
Is aware of the learning, knowledge acquisition, and data collection methods used in data analysis, as well as their ethical limitations and problem-solving techniques.
Can identify business problems to which data science or machine learning solutions can be applied.
Can prototype possible solutions, visualize results, and identify business value to inform decision-making and guide further analysis.
Can apply learned theories and methods to explore, systematize, and analyze facts and relationships; formulate independent conclusions and critical observations; propose and evaluate decisions in both routine and partially unknown domestic and international contexts.
Are able to determine the complex consequences of economic processes and organizational events.
Can apply data analysis problem solving techniques, problem solving methods, their application conditions and limitations.
Collaborate effectively with instructors and peers to expand collective knowledge.
Continuously develops expertise through independent and ongoing learning.
Demonstrates openness to and proficiency in using information technology tools.
Shows problem sensitivity, proactive behavior, and constructive cooperation in projects and group tasks to ensure high-quality outcomes.
Strives for accuracy and error-free problem solving.
Works independently with responsibility, including the selection of appropriate methodologies and techniques, as well as the organization, planning, and management of tasks.
Collects, systematizes, analyzes, and evaluates data effectively while fostering both general and professional growth.
Applies a systems-oriented approach to thinking and problem solving.
Takes full responsibility for analyses, conclusions, and decisions.

Course outcome (names and codes of related subjects):

none

Prerequisites for course registration and completion: (CODE):

none

In the case of multi-semester courses, position on the possibility of and conditions for concurrent registration:

none

The number of students required to start the course (minimum, maximum), student selection method:

all students admitted

Detailed course syllabus (if the course can be divided into modules, please indicate):

Each lecture – 45 minutes

Lecture 1:

Introduction to Data Science – History, key concepts, objectives
Overview of job roles, tools, and fields of application (with examples from pharmaceutical and business contexts)

Lecture 2:

Data Discovery – Identifying and sourcing relevant datasets
Examples from pharmaceutical innovation, healthcare, and market analysis

Lecture 3:

Data Preparation – Cleaning, transforming, and organizing data
Practical exercise with a sample dataset

Lecture 4:

Data Visualization I – Principles of effective visual communication
Introduction to visualization tools (e.g., Tableau, Power BI, Python libraries)

Lecture 5:

Data Visualization I (continued) – Basic chart types, best practices, and common pitfalls
Hands-on: Creating clear and informative visuals

Lecture 6:

Supervised Machine Learning I – Concept of supervised learning
k-Nearest Neighbors (kNN): Theory and example applications

Lecture 7:

Supervised Machine Learning I (continued) – Decision Trees: concept, strengths, and limitations
Short practical demo with sample data

Lecture 8:

Supervised Machine Learning II – Introduction to ensemble methods (bagging, boosting, random forests)

Lecture 9:

Supervised Machine Learning II (continued) – Neural Networks: basic architecture and applications in pharma/business

Lecture 10:

Model Evaluation and Validation – Performance metrics (accuracy, precision, recall, F1-score)

Lecture 11:

Model Evaluation and Validation (continued) – Cross-validation, train/test splits, avoiding overfitting
Short lab exercise to compare models

Lecture 12:

Unsupervised Machine Learning – Concept and applications
k-Means clustering: theory and process

Lecture 13:

Unsupervised Machine Learning (continued) – Practical example with clustering in a business or pharma dataset

Lecture 14:

Integrated Case Study – From data discovery to visualization and modelling
Applying both supervised and unsupervised methods in a real-world scenario

Lecture 15:

Review and Discussion – Key takeaways, open Q&A, industry applications
Guidance for further study and project work

none

Requirements for attendance, options for making up missed sessions, and method of absence justification:

Full attendance is required. Completing additional e-learning materials are required to make up missed courses.

Assessment methods during semester (number, topics, and dates of midterms and reports, method of inclusion in the course grade, opportunities for make-up and improvement of marks):

test and project work submitted at the end of the semester

Number and type of individual assignments to be completed, submission deadlines:

–

Requirements for the successful completion of the course:

successful completion of the test at the end of the semester (>50%)

Type of assessment:

test

same as course syllabus

Written end-term test: during the semester, the course material will be tested with a written end-term test. The test consists of theoretical questions and calculations. At least 50% of the points of the mid-term test must be obtained in order to obtain the signature and pass the course.

test: 70% theoretical, 30% practical

Grading Scale

Excellent: 90–100
Good: 80–89
Satisfactory: 65–79
Pass: 50–64
Fail: <50

Signature of habilitated instructor (course coordinator) announcing the course:

Prof. Dr. Péter Ferdinandy
Head of Department

Dr. Roland Molontay
Director

Signature of the director of the host institution:

Prof. Dr. Péter Ferdinandy
Head of Department

Date of submission:

11th August 2025

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