# Introducing PoPy

## Overview

PoPy (pronounced pop-eye, as in the sailor man) is new software to support population modelling of PK/PD (pharmacokinetic/pharmacodynamic) data using the Python Programming Language.

PoPy’s intuitive interface and automated visualization make it perfect for people who are new to PK/PD. For experienced analysts, PoPy contains all of the features that are required for modern PK/PD modelling:-

- Nonlinear mixed-effect models
- Bolus and infusion dosing regimens
- Inter-occasion variability
- Continuous and categorical likelihoods
- PK and PD models

PoPy consists of a powerful set of Command Line Tools and script files, to enable you to analyse a PK/PD data file quickly and efficiently, whilst minisimising errors due to an elegant and intuitive syntax.

PoPy makes it easy to fit models to data, but also has the ability to generate new data to test hypothetical scenarios.

## Documentation Structure

See Table 1 for layout of this documentation:-

Table 1 PoPy Documentation Structure
Section | Contents
--- | ---
Getting Started Guide | Read this first to get familiar with PoPy
Principles of Pharmacokinetics | A summary of individual PK/PD models using PoPy syntax
Population Models in PoPy | A summary of population PK models using PoPy syntax
PoPy Example Models | Examples of using PoPy for PK/PD analysis
PoPy for Nonmem Users | Guidance for converting Nonmem examples to PoPy
PoPy Reference Guide | A comprehensive reference on PoPy tools and scripts
Appendices | Links to PK/PD terms used throughout this guide.

## System Requirements

PoPy is currently available as a 64 bit Microsoft Windows binary. See Table 2:-

Table 2 System Requirements for PoPy
Operating System | Microsoft Windows 7.0/8.0/10.0
Disk Space Required | 1.5Gb
Bit Size | 64
Processor | Intel Core i3 and better recommended
Binary Installer Size | ~250Mb

## Abbreviations

FOCE
  first order conditional estimation
IMP
  importance sampling
ITS
  iterative two stage
IV
  Intra-venous, i.e. injected directly into a vein
JOE
  joint optimisation and estimation
LAPLACE
  Laplace approximation
OBJV
  objective function value
ODEs
  ordinary differential equations
PK/PD
  Pharmacokinetic/Pharmacodynamic
SAEM
  stochastic approximation expectation maximisation
TSLD
  Time since last dose
VPC
  visual predictive check
wrt
  With respect to - usually defines rate of change variable in an ordinary differential equation

## Next Steps

See Install PoPy.