PM1MSP: Mathematics and Statistics for Pharmacology

Module code: PM1MSP

Module provider: Pharmacy; School of Chemistry, Food and Pharmacy

Credits: 20

Level: Level 1 (Certificate)

When you'll be taught: Semester 2

Module convenor: Dr Julia Abery, email: j.abery@reading.ac.uk

Pre-requisite module(s):

Co-requisite module(s): IN THE SAME YEAR AS TAKING THIS MODULE YOU MUST TAKE PM1PY2 AND TAKE PM1PY3 AND TAKE PM1PDA AND TAKE PM1KSP (Compulsory)

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: No

Talis reading list: Yes

Last updated: 19 September 2024

Overview

Module aims and purpose

This module will provide you with an introduction to basic mathematical and statistical concepts relevant to pharmacology. The module is designed to ensure you have the required quantitative skills for application in your first-year pharmacological modules as well as providing you with relevant foundation material for the second year Mathematical Modelling for Pharmacology module. The module will provide you with an overview of functions, basic algebra, differentiation, integration, ordinary differential equations, matrices and vectors, exploratory data analysis, statistical inference, basic experimental design and an introduction to medical statistics used in clinical trials.

This module aims to provide students with the necessary skills for undertaking basic quantitative analysis in pharmacology. It also includes an introduction to a mathematical/statistical computing package.

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Plot functions relevant to pharmacological students
2. Understand the role of the calculus in informing pharmacological studies
3. Manipulate matrices and vectors and undertake simple operations relevant to data analysis
4. Understand methods for obtaining, exploring, estimating and testing pharmacological data, recognising the statistical concepts of precision and statistical significance
5. Work in small groups to improve team-working skills, such as leadership, motivating and working with others, and contribute to identifying the learning and development needs of team members through coaching and feedback
6. Develop effective communication within a team and communicate findings to a wider audience

Module content

• Motivation for the use of mathematics and statistics in drug discovery and development
• Functions and basic algebra for pharmacological applications (algebra, polynomials in a single variable, roots of quadratic equations, factorising, plotting graphs, logs and the exponential function, trigonometric functions, solving simultaneous equations)
• Differentiation as a prelude to learning about differential equations (differentiation of polynomials, exponential and log functions, product rule, chain rule)
• Integration as the anti-derivative and a sum (Integration of polynomials, simple functions, exponential, integration by substitution)
• Ordinary differential equations for formulating mathematical models (Formulating and solving linear first order equations)
• Matrices & vectors (matrix operations; determinants, inverse)
• Probability and Statistics for understanding data: use of probability distributions, exploratory data analysis including graphical displays of data
• Concept of sampling distributions
• Estimation and confidence intervals for means and proportions
• Hypothesis testing of continuous and categorical data
• Basic experimental design (Completely Randomised Designs and Randomised Block Designs)
• Introduction to medical statistics

Structure

Teaching and learning methods

The course content will be provided through a mixture of formal lectures, interactive workshops using appropriate case studies, supported by tutorial sessions.

Supplementary information and reading lists will be provided by the lecturers and the available facilities for computer-aided literature searching for related material will enable students to improve independent-learning skills.

Workshops and exercises associated with the module will reinforce fundamental concepts of mathematics and statistics which underpin therapeutic and pharmaceutical areas.

Study hours

At least 56 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 40% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Formative assessment and associated feedback form a large proportion of the module, with students being provided with workshops, tutorials and online assessments to prepare for the final examination. Formative assessment is provided through compulsory small group tutorials and workshops, instructor-, self-, and peer-led assessment and feedback.

Reassessment

Additional costs