.. _indiv_pkpd_index:

Simulating A Single Individual
################################

In the first of several "how-to" style guides, we present some of the more
fundamental concepts in pharmacokinetics and pharmacodynamics, using |popy|
scripts to simulate data and figures to support our explanations.
For ease of introduction, we consider only a single individual and look at 
populations of individuals in following parts (|eg| :ref:`pop_pkpd_index`).

Pharmacokinetics 
([MouldUpton2012]_, [MouldUpton2013]_, [UptonMould2013]_, [RowlandTozer2012]_)
is the study of the time profile of drugs and metabolites 
in a physiological organism, often described as "what the body does to the drug". 

Typically, our goal is to build (or grow) a mathematical model 
that predicts a time course of *drug* concentration 
resembling the observations we have collected from an individual.

Pharmacodynamics, in contrast, describes "what the drug does to the body" and
typically requires us to build a mathematical model 
that predicts a time course of *biomarker* concentration
resembling the observations we have collected from an individual.

Often, the two are combined in a single model because the biomarker predictions
are dependent (either directly or indirectly) on the drug predictions.

In this part, we consider factors of our mathematical model such as how
drug is administered, distributed and eliminated, and also any errors in 
the way we observe |pkpd| concentrations.

We assume little prior knowledge of |pkpd|, and experienced modellers may be
tempted to skip this section. It is, however, a gentle introduction to the 
syntax of |popy| models and how they are grown, bit by bit.

.. toctree::
    :maxdepth: 1

    dosing_examples
    elimination
    compartment_models
    error_models

    .. valid but probably need lots of work

    pharmacodynamic_models
    .. disease_progression_models
    .. simultaneous_vs_separate_fit
