{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8f6c8427-d54f-41f0-a74b-061f4bca237c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from functools import partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ebda0d0-711f-4cd3-a50a-e6c6b820f06b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cost_of_capital = 1.11\n",
    "xlabels = ['Hit Discovery', 'Hit-to-Lead', 'Lead Optimization', 'Preclinical', 'Clinical Phase 1', 'Clinical Phase 2', 'Clinical Phase 3', 'Approval', 'Total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5dd5848d-d403-4403-b2e0-2f4d6e558d9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_WIP(pTS: list):\n",
    "    prev = 1\n",
    "    WIP = []\n",
    "    for p in reversed(pTS):\n",
    "        prev = prev / p\n",
    "        WIP.append(prev)\n",
    "    return list(reversed(WIP))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce8dd60e-fa42-462c-b7fb-0381b015bd84",
   "metadata": {},
   "source": [
    "### Analysis 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "41d9d5fe-6851-4488-b577-05a08989f45f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Stage</th>\n",
       "      <th>Stage</th>\n",
       "      <th>p(TS)</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Time</th>\n",
       "      <th>WIP needed</th>\n",
       "      <th>Cost/Launch</th>\n",
       "      <th>% Cost/NME</th>\n",
       "      <th>Cost/launch, capitalized</th>\n",
       "      <th>Expended Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hit Discovery</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.297893</td>\n",
       "      <td>24.297893</td>\n",
       "      <td>0.027832</td>\n",
       "      <td>99.409717</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hit-to-Lead</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>19.438315</td>\n",
       "      <td>48.595787</td>\n",
       "      <td>0.055664</td>\n",
       "      <td>179.116607</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LO</td>\n",
       "      <td>0.85</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.578736</td>\n",
       "      <td>145.787361</td>\n",
       "      <td>0.166992</td>\n",
       "      <td>459.486378</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pre</td>\n",
       "      <td>0.69</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.391926</td>\n",
       "      <td>61.959628</td>\n",
       "      <td>0.070972</td>\n",
       "      <td>158.495017</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.54</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8.550429</td>\n",
       "      <td>128.256431</td>\n",
       "      <td>0.146911</td>\n",
       "      <td>295.571789</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>0.34</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.617232</td>\n",
       "      <td>184.689260</td>\n",
       "      <td>0.211552</td>\n",
       "      <td>363.949397</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>0.70</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.569859</td>\n",
       "      <td>235.478807</td>\n",
       "      <td>0.269729</td>\n",
       "      <td>357.473409</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Approval</td>\n",
       "      <td>0.91</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.098901</td>\n",
       "      <td>43.956044</td>\n",
       "      <td>0.050349</td>\n",
       "      <td>51.404728</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Stage          Stage  p(TS)   Cost  Time  WIP needed  Cost/Launch  % Cost/NME  \\\n",
       "0      Hit Discovery   0.80    1.0   1.0   24.297893    24.297893    0.027832   \n",
       "1        Hit-to-Lead   0.75    2.5   1.5   19.438315    48.595787    0.055664   \n",
       "2                 LO   0.85   10.0   2.0   14.578736   145.787361    0.166992   \n",
       "3                Pre   0.69    5.0   1.0   12.391926    61.959628    0.070972   \n",
       "4                  1   0.54   15.0   1.5    8.550429   128.256431    0.146911   \n",
       "5                  2   0.34   40.0   2.5    4.617232   184.689260    0.211552   \n",
       "6                  3   0.70  150.0   2.5    1.569859   235.478807    0.269729   \n",
       "7           Approval   0.91   40.0   1.5    1.098901    43.956044    0.050349   \n",
       "\n",
       "Stage  Cost/launch, capitalized  Expended Time  \n",
       "0                     99.409717            0.0  \n",
       "1                    179.116607            1.0  \n",
       "2                    459.486378            2.5  \n",
       "3                    158.495017            4.5  \n",
       "4                    295.571789            5.5  \n",
       "5                    363.949397            7.0  \n",
       "6                    357.473409            9.5  \n",
       "7                     51.404728           12.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('DD_cost_savings.csv').set_index('Stage').transpose().reset_index(drop=False, names='Stage').drop(index=8)\n",
    "df['Expended Time'] = [0] + df.Time.cumsum().tolist()[:-1] # Shifted cumsum\n",
    "REFERENCE_TOTAL = df['Cost/launch, capitalized'].sum() # Total\n",
    "TOTAL_TIME = 13.5\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "118fe00f-52b9-4c23-bc03-bc7e1a7f6eeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Stage</th>\n",
       "      <th>Stage</th>\n",
       "      <th>p(TS)</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Time</th>\n",
       "      <th>WIP needed</th>\n",
       "      <th>Cost/Launch</th>\n",
       "      <th>% Cost/NME</th>\n",
       "      <th>Cost/launch, capitalized</th>\n",
       "      <th>Expended Time</th>\n",
       "      <th>Cost 20% Net</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hit Discovery</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.297893</td>\n",
       "      <td>24.297893</td>\n",
       "      <td>0.027832</td>\n",
       "      <td>99.409717</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.881943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hit-to-Lead</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>19.438315</td>\n",
       "      <td>48.595787</td>\n",
       "      <td>0.055664</td>\n",
       "      <td>179.116607</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.823321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LO</td>\n",
       "      <td>0.85</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.578736</td>\n",
       "      <td>145.787361</td>\n",
       "      <td>0.166992</td>\n",
       "      <td>459.486378</td>\n",
       "      <td>2.5</td>\n",
       "      <td>91.897276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pre</td>\n",
       "      <td>0.69</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.391926</td>\n",
       "      <td>61.959628</td>\n",
       "      <td>0.070972</td>\n",
       "      <td>158.495017</td>\n",
       "      <td>4.5</td>\n",
       "      <td>31.699004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.54</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8.550429</td>\n",
       "      <td>128.256431</td>\n",
       "      <td>0.146911</td>\n",
       "      <td>295.571789</td>\n",
       "      <td>5.5</td>\n",
       "      <td>59.114358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>0.34</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.617232</td>\n",
       "      <td>184.689260</td>\n",
       "      <td>0.211552</td>\n",
       "      <td>363.949397</td>\n",
       "      <td>7.0</td>\n",
       "      <td>72.789879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>0.70</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.569859</td>\n",
       "      <td>235.478807</td>\n",
       "      <td>0.269729</td>\n",
       "      <td>357.473409</td>\n",
       "      <td>9.5</td>\n",
       "      <td>71.494682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Approval</td>\n",
       "      <td>0.91</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.098901</td>\n",
       "      <td>43.956044</td>\n",
       "      <td>0.050349</td>\n",
       "      <td>51.404728</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.280946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Stage          Stage  p(TS)   Cost  Time  WIP needed  Cost/Launch  % Cost/NME  \\\n",
       "0      Hit Discovery   0.80    1.0   1.0   24.297893    24.297893    0.027832   \n",
       "1        Hit-to-Lead   0.75    2.5   1.5   19.438315    48.595787    0.055664   \n",
       "2                 LO   0.85   10.0   2.0   14.578736   145.787361    0.166992   \n",
       "3                Pre   0.69    5.0   1.0   12.391926    61.959628    0.070972   \n",
       "4                  1   0.54   15.0   1.5    8.550429   128.256431    0.146911   \n",
       "5                  2   0.34   40.0   2.5    4.617232   184.689260    0.211552   \n",
       "6                  3   0.70  150.0   2.5    1.569859   235.478807    0.269729   \n",
       "7           Approval   0.91   40.0   1.5    1.098901    43.956044    0.050349   \n",
       "\n",
       "Stage  Cost/launch, capitalized  Expended Time  Cost 20% Net  \n",
       "0                     99.409717            0.0     19.881943  \n",
       "1                    179.116607            1.0     35.823321  \n",
       "2                    459.486378            2.5     91.897276  \n",
       "3                    158.495017            4.5     31.699004  \n",
       "4                    295.571789            5.5     59.114358  \n",
       "5                    363.949397            7.0     72.789879  \n",
       "6                    357.473409            9.5     71.494682  \n",
       "7                     51.404728           12.0     10.280946  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate Cost Saving\n",
    "def cost20_saving(x: pd.Series, total_time=13.5):\n",
    "    y = x['Cost/launch, capitalized']\n",
    "    \n",
    "    reduced_cost = x['Cost/Launch'] * 0.8\n",
    "    reduced_cap = reduced_cost * (cost_of_capital**(TOTAL_TIME-x['Expended Time']))\n",
    "    return y - reduced_cap\n",
    "\n",
    "df['Cost 20% Net'] = df.apply(cost20_saving, axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "12c88e81-17bf-4c19-ab12-266dd11cf52f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Stage</th>\n",
       "      <th>Stage</th>\n",
       "      <th>p(TS)</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Time</th>\n",
       "      <th>WIP needed</th>\n",
       "      <th>Cost/Launch</th>\n",
       "      <th>% Cost/NME</th>\n",
       "      <th>Cost/launch, capitalized</th>\n",
       "      <th>Expended Time</th>\n",
       "      <th>Cost 20% Net</th>\n",
       "      <th>Fail 20% Net</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hit Discovery</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.297893</td>\n",
       "      <td>24.297893</td>\n",
       "      <td>0.027832</td>\n",
       "      <td>99.409717</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.881943</td>\n",
       "      <td>4.733796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hit-to-Lead</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>19.438315</td>\n",
       "      <td>48.595787</td>\n",
       "      <td>0.055664</td>\n",
       "      <td>179.116607</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.823321</td>\n",
       "      <td>17.407895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LO</td>\n",
       "      <td>0.85</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.578736</td>\n",
       "      <td>145.787361</td>\n",
       "      <td>0.166992</td>\n",
       "      <td>459.486378</td>\n",
       "      <td>2.5</td>\n",
       "      <td>91.897276</td>\n",
       "      <td>25.159524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pre</td>\n",
       "      <td>0.69</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.391926</td>\n",
       "      <td>61.959628</td>\n",
       "      <td>0.070972</td>\n",
       "      <td>158.495017</td>\n",
       "      <td>4.5</td>\n",
       "      <td>31.699004</td>\n",
       "      <td>73.914200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.54</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8.550429</td>\n",
       "      <td>128.256431</td>\n",
       "      <td>0.146911</td>\n",
       "      <td>295.571789</td>\n",
       "      <td>5.5</td>\n",
       "      <td>59.114358</td>\n",
       "      <td>173.530561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>0.34</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.617232</td>\n",
       "      <td>184.689260</td>\n",
       "      <td>0.211552</td>\n",
       "      <td>363.949397</td>\n",
       "      <td>7.0</td>\n",
       "      <td>72.789879</td>\n",
       "      <td>435.160626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>0.70</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.569859</td>\n",
       "      <td>235.478807</td>\n",
       "      <td>0.269729</td>\n",
       "      <td>357.473409</td>\n",
       "      <td>9.5</td>\n",
       "      <td>71.494682</td>\n",
       "      <td>151.065972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Approval</td>\n",
       "      <td>0.91</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.098901</td>\n",
       "      <td>43.956044</td>\n",
       "      <td>0.050349</td>\n",
       "      <td>51.404728</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.280946</td>\n",
       "      <td>38.112421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Stage          Stage  p(TS)   Cost  Time  WIP needed  Cost/Launch  % Cost/NME  \\\n",
       "0      Hit Discovery   0.80    1.0   1.0   24.297893    24.297893    0.027832   \n",
       "1        Hit-to-Lead   0.75    2.5   1.5   19.438315    48.595787    0.055664   \n",
       "2                 LO   0.85   10.0   2.0   14.578736   145.787361    0.166992   \n",
       "3                Pre   0.69    5.0   1.0   12.391926    61.959628    0.070972   \n",
       "4                  1   0.54   15.0   1.5    8.550429   128.256431    0.146911   \n",
       "5                  2   0.34   40.0   2.5    4.617232   184.689260    0.211552   \n",
       "6                  3   0.70  150.0   2.5    1.569859   235.478807    0.269729   \n",
       "7           Approval   0.91   40.0   1.5    1.098901    43.956044    0.050349   \n",
       "\n",
       "Stage  Cost/launch, capitalized  Expended Time  Cost 20% Net  Fail 20% Net  \n",
       "0                     99.409717            0.0     19.881943      4.733796  \n",
       "1                    179.116607            1.0     35.823321     17.407895  \n",
       "2                    459.486378            2.5     91.897276     25.159524  \n",
       "3                    158.495017            4.5     31.699004     73.914200  \n",
       "4                    295.571789            5.5     59.114358    173.530561  \n",
       "5                    363.949397            7.0     72.789879    435.160626  \n",
       "6                    357.473409            9.5     71.494682    151.065972  \n",
       "7                     51.404728           12.0     10.280946     38.112421  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate Amortized Cost of Failure if 20% dropped at each stage\n",
    "fail_20_net = []\n",
    "for i, row in df.iterrows():\n",
    "    # Drop failure rate by 20%\n",
    "    adj_pTS = 1 - ((1 - row['p(TS)'])*0.8)\n",
    "\n",
    "    # Recalculate WIP\n",
    "    adj_pTS_traj = df['p(TS)'].tolist()\n",
    "    adj_pTS_traj[i] = adj_pTS\n",
    "    adj_WIP = calculate_WIP(adj_pTS_traj)\n",
    "\n",
    "    # Adjusted cost\n",
    "    adj_cost = df['Cost'] * adj_WIP\n",
    "\n",
    "    # Adjusted cap\n",
    "    adj_cap = adj_cost * (cost_of_capital**(TOTAL_TIME - df['Expended Time']))\n",
    "    amortized_cap = adj_cap.sum()\n",
    "\n",
    "    # Net saving\n",
    "    net_saving = REFERENCE_TOTAL - amortized_cap\n",
    "    fail_20_net.append(net_saving)\n",
    "df['Fail 20% Net'] = fail_20_net\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "319cff1b-7138-4e66-9267-bb7ac62bb238",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Stage</th>\n",
       "      <th>Stage</th>\n",
       "      <th>p(TS)</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Time</th>\n",
       "      <th>WIP needed</th>\n",
       "      <th>Cost/Launch</th>\n",
       "      <th>% Cost/NME</th>\n",
       "      <th>Cost/launch, capitalized</th>\n",
       "      <th>Expended Time</th>\n",
       "      <th>Cost 20% Net</th>\n",
       "      <th>Fail 20% Net</th>\n",
       "      <th>Speed 20% Net</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hit Discovery</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.297893</td>\n",
       "      <td>24.297893</td>\n",
       "      <td>0.027832</td>\n",
       "      <td>99.409717</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.881943</td>\n",
       "      <td>4.733796</td>\n",
       "      <td>2.053376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hit-to-Lead</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>19.438315</td>\n",
       "      <td>48.595787</td>\n",
       "      <td>0.055664</td>\n",
       "      <td>179.116607</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.823321</td>\n",
       "      <td>17.407895</td>\n",
       "      <td>8.585013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LO</td>\n",
       "      <td>0.85</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.578736</td>\n",
       "      <td>145.787361</td>\n",
       "      <td>0.166992</td>\n",
       "      <td>459.486378</td>\n",
       "      <td>2.5</td>\n",
       "      <td>91.897276</td>\n",
       "      <td>25.159524</td>\n",
       "      <td>30.173445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pre</td>\n",
       "      <td>0.69</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.391926</td>\n",
       "      <td>61.959628</td>\n",
       "      <td>0.070972</td>\n",
       "      <td>158.495017</td>\n",
       "      <td>4.5</td>\n",
       "      <td>31.699004</td>\n",
       "      <td>73.914200</td>\n",
       "      <td>18.517986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.54</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8.550429</td>\n",
       "      <td>128.256431</td>\n",
       "      <td>0.146911</td>\n",
       "      <td>295.571789</td>\n",
       "      <td>5.5</td>\n",
       "      <td>59.114358</td>\n",
       "      <td>173.530561</td>\n",
       "      <td>36.743448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>0.34</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.617232</td>\n",
       "      <td>184.689260</td>\n",
       "      <td>0.211552</td>\n",
       "      <td>363.949397</td>\n",
       "      <td>7.0</td>\n",
       "      <td>72.789879</td>\n",
       "      <td>435.160626</td>\n",
       "      <td>79.111628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>0.70</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.569859</td>\n",
       "      <td>235.478807</td>\n",
       "      <td>0.269729</td>\n",
       "      <td>357.473409</td>\n",
       "      <td>9.5</td>\n",
       "      <td>71.494682</td>\n",
       "      <td>151.065972</td>\n",
       "      <td>97.286293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Approval</td>\n",
       "      <td>0.91</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.098901</td>\n",
       "      <td>43.956044</td>\n",
       "      <td>0.050349</td>\n",
       "      <td>51.404728</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.280946</td>\n",
       "      <td>38.112421</td>\n",
       "      <td>60.564298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Stage          Stage  p(TS)   Cost  Time  WIP needed  Cost/Launch  % Cost/NME  \\\n",
       "0      Hit Discovery   0.80    1.0   1.0   24.297893    24.297893    0.027832   \n",
       "1        Hit-to-Lead   0.75    2.5   1.5   19.438315    48.595787    0.055664   \n",
       "2                 LO   0.85   10.0   2.0   14.578736   145.787361    0.166992   \n",
       "3                Pre   0.69    5.0   1.0   12.391926    61.959628    0.070972   \n",
       "4                  1   0.54   15.0   1.5    8.550429   128.256431    0.146911   \n",
       "5                  2   0.34   40.0   2.5    4.617232   184.689260    0.211552   \n",
       "6                  3   0.70  150.0   2.5    1.569859   235.478807    0.269729   \n",
       "7           Approval   0.91   40.0   1.5    1.098901    43.956044    0.050349   \n",
       "\n",
       "Stage  Cost/launch, capitalized  Expended Time  Cost 20% Net  Fail 20% Net  \\\n",
       "0                     99.409717            0.0     19.881943      4.733796   \n",
       "1                    179.116607            1.0     35.823321     17.407895   \n",
       "2                    459.486378            2.5     91.897276     25.159524   \n",
       "3                    158.495017            4.5     31.699004     73.914200   \n",
       "4                    295.571789            5.5     59.114358    173.530561   \n",
       "5                    363.949397            7.0     72.789879    435.160626   \n",
       "6                    357.473409            9.5     71.494682    151.065972   \n",
       "7                     51.404728           12.0     10.280946     38.112421   \n",
       "\n",
       "Stage  Speed 20% Net  \n",
       "0           2.053376  \n",
       "1           8.585013  \n",
       "2          30.173445  \n",
       "3          18.517986  \n",
       "4          36.743448  \n",
       "5          79.111628  \n",
       "6          97.286293  \n",
       "7          60.564298  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate speed saving\n",
    "# NOTE SPEED reported is additional profits from more patent time\n",
    "# HERE we reported capitalized savings\n",
    "\n",
    "speed_20_net = []\n",
    "for i, row in df.iterrows():\n",
    "    # Drop speed by 20%\n",
    "    adj_time = row['Time']*0.8\n",
    "\n",
    "    # Adjust expended time\n",
    "    adj_time_traj = df['Time'].copy()\n",
    "    adj_time_traj[i] = adj_time\n",
    "\n",
    "    # Adjust time\n",
    "    adj_total_time = adj_time_traj.sum()\n",
    "    adj_exp_time = pd.Series([0] + adj_time_traj.cumsum().tolist()[:-1]) # Shifted cumsum\n",
    "\n",
    "    # Adjusted Capital\n",
    "    adj_cap = df['Cost/Launch'] * (cost_of_capital**(adj_total_time - adj_exp_time))\n",
    "    amortized_cap = adj_cap.sum()\n",
    "\n",
    "    # Net saving\n",
    "    net_saving = REFERENCE_TOTAL - amortized_cap\n",
    "    speed_20_net.append(net_saving)\n",
    "    #if i == 0:\n",
    "    #    break\n",
    "    \n",
    "df['Speed 20% Net'] = speed_20_net\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a988fd4b-8bb0-4031-819b-3221f147a87a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create total stage\n",
    "df.columns = df.columns.tolist()\n",
    "total_row = df.sum(axis=0)\n",
    "total_row['Stage'] = 'Total'\n",
    "total_row\n",
    "df = pd.concat([df, pd.DataFrame(total_row).transpose()], axis=0, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "685d88e8-9697-4182-9f7b-63e277796129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f784f18e1a0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "tdf = df.melt(id_vars=['Stage'], value_name='Net Saving', var_name='Type', value_vars=['Cost 20% Net', 'Fail 20% Net', 'Speed 20% Net'])\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_palette(\"colorblind\")\n",
    "\n",
    "pl = sns.barplot(\n",
    "    tdf,\n",
    "    x='Stage',\n",
    "    y='Net Saving',\n",
    "    dodge=True,\n",
    "    hue='Type',\n",
    "    hue_order=['Speed 20% Net', 'Fail 20% Net', 'Cost 20% Net'],\n",
    "    edgecolor='k',\n",
    "    gap=0.2\n",
    ")\n",
    "sns.despine(left=True)\n",
    "plt.xticks(rotation=45, ha='right', labels=xlabels, ticks=range(len(xlabels)));\n",
    "\n",
    "plt.xlabel('Phase of Drug Discovery and Development')\n",
    "plt.ylabel('Net Savings Compared to Baseline\\n(in $m at time of approval)')\n",
    "\n",
    "plt.legend(\n",
    "    title='',\n",
    "    handles=pl.get_legend_handles_labels()[0],\n",
    "    labels=['Speed - Time reduced by 20%', 'Quality - Failure rate reduced by 20%', 'Cost - Cost reduced by 20%'],\n",
    "    frameon=True,\n",
    "    framealpha=1.0,\n",
    "    fancybox=False,\n",
    "    edgecolor='w',\n",
    ")\n",
    "\n",
    "#plt.savefig('Capital_cost_saving.png', bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "434bee55-ac9a-4c04-97e6-7004d2b16723",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('DD_cost_savings_out.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "747dfac6-e3fc-4e3a-9990-7ea23ac07158",
   "metadata": {},
   "source": [
    "### Analysis 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4db6d4e6-15aa-4e32-b4b5-0e4ea474ff1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Stage</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Without Biomarkers</th>\n",
       "      <th>With Biomarkers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hit Discovery</td>\n",
       "      <td>228.337210</td>\n",
       "      <td>121.157182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>H2L</td>\n",
       "      <td>411.418396</td>\n",
       "      <td>218.301229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LO</td>\n",
       "      <td>1055.408271</td>\n",
       "      <td>560.006370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Preclinical</td>\n",
       "      <td>364.052037</td>\n",
       "      <td>193.168336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Clinical Phase 1</td>\n",
       "      <td>678.907854</td>\n",
       "      <td>360.232844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Clinical Phase 2</td>\n",
       "      <td>537.185859</td>\n",
       "      <td>365.533834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Clinical Phase 3</td>\n",
       "      <td>415.894509</td>\n",
       "      <td>407.604253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Approval</td>\n",
       "      <td>50.407654</td>\n",
       "      <td>50.407654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Total</td>\n",
       "      <td>3741.611790</td>\n",
       "      <td>2276.411701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Stage             Stage  Without Biomarkers  With Biomarkers\n",
       "0         Hit Discovery          228.337210       121.157182\n",
       "1                   H2L          411.418396       218.301229\n",
       "2                    LO         1055.408271       560.006370\n",
       "3           Preclinical          364.052037       193.168336\n",
       "4      Clinical Phase 1          678.907854       360.232844\n",
       "5      Clinical Phase 2          537.185859       365.533834\n",
       "6      Clinical Phase 3          415.894509       407.604253\n",
       "7              Approval           50.407654        50.407654\n",
       "8                 Total         3741.611790      2276.411701"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('DD_biomarker_savings.csv').set_index('Stage').transpose().reset_index(drop=False, names='Stage')\n",
    "df.rename(columns={'Cost/launch using biomarkers, capitalized': 'With Biomarkers', 'Cost/launch not using biomarkers, capitalized': 'Without Biomarkers'}, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3c5dc693-b5d0-4ff2-af55-35e424602bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tdf = df.melt(id_vars=['Stage'], var_name='Biomarker', value_name='Cost/launch')\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_palette(\"colorblind\")\n",
    "\n",
    "pl = sns.barplot(\n",
    "    tdf,\n",
    "    x='Stage',\n",
    "    y='Cost/launch',\n",
    "    dodge=True,\n",
    "    hue='Biomarker',\n",
    "    edgecolor='k',\n",
    "    width=0.5,\n",
    "    gap=0.2\n",
    ")\n",
    "sns.despine(left=True)\n",
    "plt.xticks(rotation=45, ha='right', labels=xlabels, ticks=range(len(xlabels)));\n",
    "\n",
    "plt.xlabel('Phase of Drug Discovery and Development')\n",
    "plt.ylabel('Capitalized Cost\\n(in $m at time of approval)')\n",
    "\n",
    "plt.legend(\n",
    "    title='',\n",
    "    frameon=True,\n",
    "    framealpha=1.0,\n",
    "    fancybox=False,\n",
    "    edgecolor='w',\n",
    ")\n",
    "\n",
    "#plt.savefig(\"Capital_cost_biomarkers.png\", bbox_inches='tight', dpi=300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}