{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![CDS 411 logo](../../img/cds-411-logo.png)\n",
    "\n",
    "# Class 9: System dynamics models: Drug dosage I\n",
    "\n",
    "---\n",
    "\n",
    "![CC BY-SA 4.0 license](../../img/cc-by-sa.png)\n",
    "\n",
    "This notebook is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "cell_style": "split",
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Pharmacokinetics\n",
    "\n",
    "*   Quantitative field of science\n",
    "*   Question: \"what is a correct/effective dosage for a drug?\"\n",
    "*   Influenced by multiple factors: drug absorption, distribution, metabolism, and elimination"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Drug concentration\n",
    "\n",
    "*   Amount of drug in the system\n",
    "    \\begin{equation}\n",
    "    \\dfrac{\\text{amount of drug}}{\\text{volume of blood}}\n",
    "    \\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**<p style=\"font-size: 1.5em; text-align: center;\">Why would we use drug <emph>concentration</emph> rather than the absolute amount?</p>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Minimum Effective Concentration (MEC)\n",
    "\n",
    "*   The least amount of drug that is helpful\n",
    "\n",
    "### Maximum Therapeutic Concentration (MTC)\n",
    "\n",
    "*   Alternatively referred to as \"Minimum Toxic Concentration\"\n",
    "*   Largest amount that is helpful without having dangerous or intolerable side effects\n",
    "\n",
    "### Therapeutic range\n",
    "\n",
    "*   Drug concentration between the MEC and the MTC thresholds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Drug half-life\n",
    "\n",
    "*   Amount of time it takes for half the drug to be eliminated from the body.\n",
    "    \\begin{equation}\n",
    "    r=\\dfrac{\\ln(2)}{t_{1/2}}\n",
    "    \\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Blood, plasma, and blood serum\n",
    "\n",
    "*   It's common for drug concentration and the drug half-life to be measured with respect to concentration in the plasma or the blood serum.\n",
    "*   Plasma is the fluid that contains blood cells.\n",
    "*   Blood serum is the clear fluid that separates from blood when it clots.\n",
    "\n",
    "### Adult body\n",
    "\n",
    "*   Contains approximately 5 liters of blood.\n",
    "*   Contains approximately 3 liters of plasma.\n",
    "*   Contains approximately 3 liters of blood serum."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## One-compartment model of single dose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### One-compartment model\n",
    "\n",
    "*   A simplified representation of how a body processes a drug.\n",
    "*   Model the body as a single homogeneous compartment\n",
    "*   Distribution is instantaneous\n",
    "*   Concentration is proportional to drug dosage and the volume of blood in a patient\n",
    "*   Rate of elimination is proportional to the amount of drug in the system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Basic facts about aspirin (acetylsalicylic acid)\n",
    "\n",
    "*   Dosage: Two 325 milligram tablets every 4 hours, up to 12 tablets per day\n",
    "*   Pain relief occurs at concentrations starting at 150 to 300 micrograms per milliliter of **plasma**\n",
    "*   Toxicity may occur at concentrations of 350 micrograms per milliliter\n",
    "*   Plasma half-life of a dose between 300 mg and 650 mg is 3.1 hours (smaller dose) to 3.2 hours (larger dose)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Building an aspirin model\n",
    "\n",
    "\\begin{equation}\n",
    "\\dfrac{dQ}{dt}=-rQ\n",
    "\\end{equation}\n",
    "\n",
    "*   Assume an adult is taking two 325 milligram tablets\n",
    "*   Assume a one-compartment model where aspirin is immediately absorbed into the plasma\n",
    "*   Use upper-bound of aspirin half-life, 3.2 hours\n",
    "*   MEC of 150 micrograms per milliliter\n",
    "*   MTC of 350 micrograms per milliliter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**<span style=\"font-size: 1.5em; text-align: center;\">What value should I use for the plasma volume?</span>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**<span style=\"font-size: 1.5em; text-align: center;\">Do I need to do any unit conversions? If so, what should be converted and what are the new values?</span>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### System dynamics diagram\n",
    "\n",
    "<center><img width=\"50%\" src=\"../../img/aspirin_system_dynamics_model.png\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Model constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_time = 8  # hours\n",
    "delta_t = 0.01  # hours\n",
    "sim_steps = int(sim_time / delta_t)\n",
    "half_life = 3.2  # hours\n",
    "plasma_volume = 3000  # milliliters\n",
    "initial_aspirin_in_plasma = 2 * 325 * 1000  # micrograms\n",
    "elimination_constant = -np.log(2) / half_life  # inverse hours\n",
    "mec = 150  # micrograms\n",
    "mtc = 350  # micrograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "### Simulation trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plasma_concentration = [[0, initial_aspirin_in_plasma / plasma_volume]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Finite difference equation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standard `for` loop for unconstrained decay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "aspirin_in_plasma = initial_aspirin_in_plasma\n",
    "for step_index in range(1, sim_steps + 1):\n",
    "    elimination = elimination_constant * aspirin_in_plasma\n",
    "    aspirin_in_plasma += elimination * delta_t\n",
    "    plasma_concentration.append([step_index, aspirin_in_plasma / plasma_volume])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Don't forget to convert your results to a data frame!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How do I extract the \"step\" column and the \"plasma\" column from `plasma_concentration`?**\n",
    "\n",
    "```python\n",
    "simulation_df = pd.DataFrame({\n",
    "    \"step\": ???,\n",
    "    \"plasma\": ???,\n",
    "})\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "List comprehensions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "simulation_df = pd.DataFrame({\n",
    "    \"step\": [x[0] for x in plasma_concentration],\n",
    "    \"plasma\": [x[1] for x in plasma_concentration],\n",
    "})\n",
    "simulation_df[\"time\"] = simulation_df[\"step\"] * delta_t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=120)  # Use dpi to change figure size in notebooks\n",
    "ax.axhline(mec, color=\"k\", linestyle=\"--\")  # Plots MEC minimum line\n",
    "ax.axhline(mtc, color=\"k\", linestyle=\"--\")  # Plots MTC maximum line\n",
    "ax.plot(simulation_df[\"time\"], simulation_df[\"plasma\"].values, \"-\")\n",
    "ax.text(7.5, mec + 10, \"MEC\")\n",
    "ax.text(7.5, mtc - 20, \"MTC\")\n",
    "ax.set_ylim(bottom=0)\n",
    "ax.set_xlabel(\"time (hr)\")\n",
    "ax.set_ylabel(r\"plasma concentration $(\\mu{}g/\\mathrm{mL})$\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### In-class practice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Work on Exercise 7 in your textbook:\n",
    "\n",
    "> How should the one-dose aspirin example be adjusted to incorporate the weight of a male patient? About 65% to 70% of a male's body is liquid. Assume that 1 kilogram of body liquid has a volume of 1 Liter. Assume the patient has a mass of 90 kilograms (comparable to about 198 pounds).\n",
    "\n",
    "You can discuss with the students near you."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Body fluid components: percentage breakdown\n",
    "\n",
    "This diagram comes from the \"Extracellular fluid\" article on *Wikipedia*, https://en.wikipedia.org/wiki/Extracellular_fluid. From this, we learn that about 7% of our body fluid consists of blood plasma.\n",
    "\n",
    "![](../../img/wikipedia_cellular_fluid_content.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Modified constants\n",
    "\n",
    "The modifications based on our gathered information are contained in the constants `body_mass`, `body_fluid_volume`, and `plasma_volume`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "sim_time = 8  # hours\n",
    "delta_t = 0.01  # hours\n",
    "sim_steps = int(sim_time / delta_t)\n",
    "half_life = 3.2  # hours\n",
    "body_mass = 90  # kilograms\n",
    "body_fluid_volume = 0.70 * body_mass * 1000  # milliliters\n",
    "plasma_volume = 0.07 * body_fluid_volume  # milliliters\n",
    "initial_aspirin_in_plasma = 2 * 325 * 1000  # micrograms\n",
    "elimination_constant = -np.log(2) / half_life  # inverse hours\n",
    "mec = 150  # micrograms\n",
    "mtc = 350  # micrograms\n",
    "plasma_concentration = [[0, initial_aspirin_in_plasma / plasma_volume]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Simulation algorithm\n",
    "\n",
    "This remains the same as last time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "aspirin_in_plasma = initial_aspirin_in_plasma\n",
    "for step_index in range(1, sim_steps + 1):\n",
    "    elimination = elimination_constant * aspirin_in_plasma\n",
    "    aspirin_in_plasma += elimination * delta_t\n",
    "    plasma_concentration.append([step_index, aspirin_in_plasma / plasma_volume])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "simulation_df = pd.DataFrame({\n",
    "    \"step\": [x[0] for x in plasma_concentration],\n",
    "    \"plasma\": [x[1] for x in plasma_concentration],\n",
    "})\n",
    "simulation_df[\"time\"] = simulation_df[\"step\"] * delta_t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Modified visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=120)  # Use dpi to change figure size in notebooks\n",
    "ax.axhline(mec, color=\"k\", linestyle=\"--\")  # Plots MEC minimum line\n",
    "ax.axhline(mtc, color=\"k\", linestyle=\"--\")  # Plots MTC maximum line\n",
    "ax.plot(simulation_df[\"time\"], simulation_df[\"plasma\"].values, \"-\")\n",
    "ax.text(7.5, mec + 10, \"MEC\")\n",
    "ax.text(7.5, mtc - 20, \"MTC\")\n",
    "ax.set_title(\"Aspirin model adjusted for 90 kilogram male\")\n",
    "ax.set_ylim(bottom=0)\n",
    "ax.set_xlabel(\"time (hr)\")\n",
    "ax.set_ylabel(r\"plasma concentration $(\\mu{}g/\\mathrm{mL})$\");"
   ]
  }
 ],
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   "language": "python",
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   "pygments_lexer": "ipython3",
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     "delete_cmd_postfix": "",
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     "library": "var_list.py",
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   "types_to_exclude": [
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