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 inf