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   "source": [
    "# Absolute IC50\n",
    "\n",
    "Be default, the calculate_ic50() method will calculate the relative IC50 value. Calculating the IC50 value can be ambiguous and frusterating. A good introduction has been written up by Dr. Keith Hornberger ([Tweetorial: IC50 vs. Ki](https://krhornberger.substack.com/p/tweetorial-ic50-vs-ki)). In the article, there are reasons given for why **IC50 depends on many factors** - the drug being tested, the conditions, which cell, etc. These can all have an influence on the final result. \n",
    "<br/>\n",
    "\n",
    "### So what is an IC50 value?\n",
    "\n",
    "On the surface, this an easy and fundamental pharmacological concept for researchers to quickly gauge the potency of a drug - 🤔 **How much of my drug is needed to inhibit my target 50%.** 💭\n",
    "\n",
    "Graphically, it will look like a sigmoidal plot (see [tutorial 001](https://github.com/tlint101/py50/blob/main/tutorials/001_single_plot.ipynb)), where typically the Y-axis are the drug responses and the X-axis is the concentration. The X-axis is typically logarithmic in order to gauge the wide variations in the tested drug concentration. We will see that **calculated IC50 can be just as ambiguous**, especially if we do not specify the response value. That is when we get **Relative vs. Absolute** values. \n",
    "\n",
    "What does **Relative vs. Absolute** mean? GraphPad has a post explaining this problem: [Relative vs. Absolute IC50](https://www.graphpad.com/support/faq/relative-vs-absolute-ic50/) and [50% of what? How exactly are Ic50 and EC50 defined?](https://www.graphpad.com/support/faqid/1356/)\n",
    "\n",
    "![graphpad_absolute.png](../img/graphpad_absolute.png)  \n",
    "Image from GraphPad KNOWLEDGEBASE - ARTICLE #1566 ([here](https://www.graphpad.com/support/faq/relative-vs-absolute-ic50/))\n",
    "\n",
    "In short, **Relative IC50 is the concentration that brings the curve down to the point halfway between the top and the bottom plateaus of the curve.** This is the most common definition. The problem is that the relative IC50 may not correlate to 50% response (Figure above). Ergo, if it is not explicitly stated that the reported IC50 is at 50% response, then it can be assumed that the reported value is \"Relative\" IC50. \n",
    "\n",
    "In contrast, **Absolute IC50 is value at the exact point where the target response is 50%**. The figure above details this point with the horizontal lines. Keep in mind that the 50% mark can also be ambiguous. The Absolute IC50 calculation by py50 depends on the data given. In short, the term Absolute IC50 may be a misnomer, as it is not the **\"absolute\" representative of the drug, but of the given dataset calculated by the program**. It may be best to use absolute IC50 values if the response can be properly converted to a percentage (0% - 100%). However, how this is done can differ between methods and assays.\n",
    " \n",
    "In this tutorial, we will not be too concerted with these aspects. Instead, we will focus on \"weird\" results that may result from the Relative vs Absolute IC50 calculation. Typically, this issue is a result of not having the recommended number of datapoints. We will explain further below. "
   ],
   "metadata": {
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   },
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  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Example start\n",
    "First we will look at a \"good example\". By this I mean there are enough points that can give a good representation of the plateau at both ends of the response. This is necessary to determine the general curve. "
   ],
   "id": "1b8a0fb9893ee2bf"
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  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
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    "ExecuteTime": {
     "end_time": "2026-05-21T04:09:55.480917Z",
     "start_time": "2026-05-21T04:09:54.593750Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "from py50 import Calculator, PlotCurve"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "source": [
    "# Import sample dataset\n",
    "good_example_df = pd.read_csv('../dataset/single_example.csv')\n",
    "\n",
    "# Instantiate Calculator() class\n",
    "good_example = Calculator(good_example_df)\n",
    "good_example.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:09:55.500780Z",
     "start_time": "2026-05-21T04:09:55.482303Z"
    }
   },
   "id": "5300b69980301bda",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Compound Name  Compound Conc  % Inhibition 1  % Inhibition 2  \\\n",
       "0        Drug 1       100000.0              90              94   \n",
       "1        Drug 1        33300.0              97              89   \n",
       "2        Drug 1        11100.0              86              89   \n",
       "3        Drug 1         3700.0              81              88   \n",
       "4        Drug 1         1240.0              63              70   \n",
       "\n",
       "   % Inhibition Avg  \n",
       "0                92  \n",
       "1                93  \n",
       "2                88  \n",
       "3                84  \n",
       "4                67  "
      ],
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       "      <th>Compound Name</th>\n",
       "      <th>Compound Conc</th>\n",
       "      <th>% Inhibition 1</th>\n",
       "      <th>% Inhibition 2</th>\n",
       "      <th>% Inhibition Avg</th>\n",
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       "      <th>1</th>\n",
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       "      <td>33300.0</td>\n",
       "      <td>97</td>\n",
       "      <td>89</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Drug 1</td>\n",
       "      <td>11100.0</td>\n",
       "      <td>86</td>\n",
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       "      <td>81</td>\n",
       "      <td>88</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Drug 1</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>63</td>\n",
       "      <td>70</td>\n",
       "      <td>67</td>\n",
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       "  </tbody>\n",
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     "execution_count": 2,
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   ],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "source": [
    "# Calculate IC50\n",
    "relative_ic50 = good_example.calculate_ic50(name_col='Compound Name', concentration_col='Compound Conc',\n",
    "                                            response_col='% Inhibition Avg')\n",
    "relative_ic50"
   ],
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    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:09:55.565708Z",
     "start_time": "2026-05-21T04:09:55.535428Z"
    }
   },
   "id": "1b5ae84e9a062cab",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  compound_name    maximum   minimum   ic50 (nM)  hill_slope\n",
       "0        Drug 1  92.854405 -7.640226  439.824243    1.040878"
      ],
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       "      <th>hill_slope</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Drug 1</td>\n",
       "      <td>92.854405</td>\n",
       "      <td>-7.640226</td>\n",
       "      <td>439.824243</td>\n",
       "      <td>1.040878</td>\n",
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     "execution_count": 3,
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   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "source": [
    "Here we read in a .csv file. The current version of py50, only requires a column with the drug name, drug concentration, and response average. As seen in the previous tutorial, the IC50 can be calculated as a table using the \"calculate_ic50\" method. By default, the IC50 value is the **Relative IC50 value**.\n",
    "\n",
    "We can double-check the calculations if we like. There are two online IC50 calculators, [AATBioquest IC50 Calculator](https://www.aatbio.com/tools/ic50-calculator) and the [Very Simple IC50 Tool Kit](http://ic50.org/index.html), both of which also give a relative IC50 value of 439.82 nM. \n",
    "\n",
    "What does that mean graphically? We will generate a box to highlight the Relative IC50 value first. For this we can use the \"conc_target\" parameter, which will take in the input concentration of interest and draw a box between the X and Y axis with the curve at the intersection. In this case, we will highlight the Relative IC50 value (439.82 nM). We will also use 'verbose=True' to see the calculations  from the backend. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "661e669088027285"
  },
  {
   "cell_type": "code",
   "source": [
    "# Instantiate PlotCurve() class\n",
    "absolute = PlotCurve(good_example_df)\n",
    "figure = absolute.curve_plot(concentration_col='Compound Conc',\n",
    "                             response_col='% Inhibition Avg',\n",
    "                             title='Relative IC50 Value',\n",
    "                             name_col='Compound Name',\n",
    "                             xlabel='Logarithmic Concentration (µM)',\n",
    "                             ylabel='Inhibition %',\n",
    "                             legend=True,\n",
    "                             box=True,\n",
    "                             box_color='red',\n",
    "                             conc_unit='µM',\n",
    "                             conc_target=0.43982,  # the relative IC50 value must be in same units as label\n",
    "                             xscale_ticks=(-3, 3),\n",
    "                             figsize=(6.4, 4.8),\n",
    "                             verbose=True)"
   ],
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    "collapsed": false,
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   },
   "id": "c6c05b1d792b84aa",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compound Name concentration will be in µM!\n",
      "Concentration on X-axis will be in µM\n",
      "Box X intersection:  0.44 µM\n",
      "Box Y intersection:  42.607 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "source": [
    "Notice that the box is not at the 50% mark on the Y-Axis. Actually it is roughly 42.607% according to the output. Again, we can check this using the AATBioquest IC50 calculator, which gives an inhibition response of 42.61% for the relative IC50 value. This data is in agreement with each other, where the IC50 value calculated is the **Relative IC50 value**. \n",
    "\n",
    "What if we are interested at exactly 50%? This number will be the **Absolute IC50** value. We can calculate that easily with the \"calculate_absolute_ic50\" function. The exact Absolute IC50 value can also be highlighted using the 'Box=True' parameter and removing the 'x_concentration=' parameter. By default, this argument will draw the box at 50% response.\n",
    "\n",
    "Bear in mind - **Absolute IC50 in this case is reflective to the input dataset being calculated**. Like the relative IC50 value, it can change depending on a number of factors. \n",
    "\n",
    "The Absolute IC50 for this dataset can be calculated and plotted as follows:"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ab2829ef5ee69677"
  },
  {
   "cell_type": "code",
   "source": [
    "absolute_ic50 = good_example.calculate_absolute_ic50(name_col='Compound Name', concentration_col='Compound Conc',\n",
    "                                                     response_col='% Inhibition Avg')\n",
    "absolute_ic50"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:09:56.318256Z",
     "start_time": "2026-05-21T04:09:56.292669Z"
    }
   },
   "id": "9d271600d451dabc",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  compound_name    maximum   minimum  relative ic50 (nM)  absolute ic50 (nM)  \\\n",
       "0        Drug 1  92.854405 -7.640226          439.824243           584.73401   \n",
       "\n",
       "   hill_slope  \n",
       "0    1.040878  "
      ],
      "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></th>\n",
       "      <th>compound_name</th>\n",
       "      <th>maximum</th>\n",
       "      <th>minimum</th>\n",
       "      <th>relative ic50 (nM)</th>\n",
       "      <th>absolute ic50 (nM)</th>\n",
       "      <th>hill_slope</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Drug 1</td>\n",
       "      <td>92.854405</td>\n",
       "      <td>-7.640226</td>\n",
       "      <td>439.824243</td>\n",
       "      <td>584.73401</td>\n",
       "      <td>1.040878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "source": [
    "absolute = PlotCurve(good_example_df)\n",
    "figure = absolute.curve_plot(concentration_col='Compound Conc',\n",
    "                             response_col='% Inhibition Avg',\n",
    "                             title='Absolute IC50 Value',\n",
    "                             name_col='Compound Name',\n",
    "                             xlabel='Logarithmic Concentration (µM)',\n",
    "                             ylabel='Inhibition %',\n",
    "                             legend=True,\n",
    "                             box=True,\n",
    "                             box_color='red',\n",
    "                             conc_unit='µM',\n",
    "                             box_intercept=50,\n",
    "                             xscale_ticks=(-3, 3),\n",
    "                             figsize=(6.4, 4.8),\n",
    "                             verbose=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:09:56.559312Z",
     "start_time": "2026-05-21T04:09:56.352724Z"
    }
   },
   "id": "a942b988bef3f59",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compound Name concentration will be in µM!\n",
      "Concentration on X-axis will be in µM\n",
      "Box X intersection:  0.585 µM\n",
      "Box Y intersection:  50 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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YEjjCVbV3mmRZgVuBVa+fXjOFXdVyZibUiUKzQp3ClWoJA0OdqBZRtWpq4leoUnkq5Omx9fxVrnqfpLWih5r49bwVgPWaKcjqR4Xe2/5TqXh0LKo11t+onPRDQU2/qkXUyG09NqEO8YQaOwAAgBhBjR0AAECMINgBAADECIIdAABAjCDYAQAAxAiCHQAAQIwg2AEAAMQI1or9l+ZX0mSWmucppWWIAAAAspvm2tTqMVp3O7l5JP0R7P6lUNe4cePseH0AAAAyTBN8a5Ly1BDs/uXNyK5CK1y4cMZLGwAAIAS0TKMqn9KzegzB7l9e86tCHcEOAABEmvR0FWPwBAAAQIyIyGB36NAht6D3N99849u2du1au/HGG90i2i1atHCLPPv73//+5/6mVq1adsMNN7j9AQAA4knEBbuDBw/avffea7/99lui0SB33nmnlShRwt58801r06aN3XXXXbZhwwZ3u851e7t27WzmzJlWrFgxu+OOO9zfAQAAxIuI6mO3atUq6927d5JA9vXXX7sauGnTplnBggXtjDPOsK+++sqFvLvvvttmzJhhNWrUsG7durn9Bw0aZBdeeKF9++23du655wbt+I4ePWqHDx8O2v0hqbx586Y5lBsAAERBsPOC2D333OOaXD3Lli2zatWquVDnqVevni1dutR3e/369X23FShQwKpXr+5uD0awU9DctGmT7dixI8v3hdQp1J122mku4AEAgCgOdp06dUp2+5YtW9ykfP6KFy/uwlZ6bs8qL9TpMRQumcA4dJNEq1l948aNVr58ecoZAIBoDnYp2b9/f5IaHF3XIIv03J4c3eZ/u+aISan51Qt1CosIrZIlS7pwd+TIEcuTJw/FDQBArAW7fPnyJWkGVSjLnz+/7/bAEKfrRYoUSfE+x48fb2PGjEnzsb0+df7NwAgdL6ArUBPsAACIwWB38sknu4EV/rZu3eprftXtuh54e9WqVVO8z+7du1vXrl2TzOqcEppfswflDABA5kXF8EPNTffjjz/agQMHfNsWL17stnu367pHTbMrV6703Z5SzZC3ygSrTQAAgFgQFcGuQYMGduqpp1r//v3d/HYTJkyw5cuXW4cOHdzt7du3tyVLlrjtul37lS1bNqhTnUSjiy++2KpUqeJOZ511ltWpU8euueYaW7BgQViOR6OLNSXNrFmzwvL4AADEuqgIdrly5bLnnnvOjX7VJMTvvvuujR071kqXLu1uV4h79tln3bx2Cnvqj6fbI61ZT/3GvvjiC3v99dfdua6H2oABA9wqHfPmzbPp06db3bp1XTO0VurI7hGvTz75pC1cuDBbHxcAgHgSsX3sfvnll0TXK1SoYK+++mqK+6t/XGp95MJNtVQ9e/a0devW+bYpkI4aNcqF1VA54YQT3EhTry9inz59XEDWJM7vvfeeZYe///7b7rvvPvfcUxvQAgAA4qDGLtop1Kkm0T/Uyfr169327G6a7Nixo/3666+2Zs0ad11NtQqYarq+7bbb3PGoGddf586dXa2oZ9KkSdawYUNXA6iaON2e0vNQ/0g1patGVUETAADEWY1dpFN/sX379qW5n5pbe/Tokey6tdqm5mLV5F166aWuyTk1wZocWUuyiUYaqyZU5s6d65qI1WSq/oupUVP46NGjbeDAgXbmmWfaM888Y4sWLbK2bdsmu79CYmBQBAAAwUewywQFsosuuigo/dR0X6rJK1q0aJr7av1bDXzIarjzas327t2bqBbv9NNPd5fTCnavvfaadenSxS6//HJ3ffDgwRHdDA4AQLygKTaTIm1gRkZ4q2xomhdPmTJlMtT/sWbNmr7rCqVa3xUAAIQXNXaZDHWqOUtPU+z8+fOtRYsWae734YcfWqNGjbKlKdYbmFKpUiXfNq3e4UnuMbTEl0dNxoFNy8k1NQMAgOxFsMskhZ9ChQqluV+zZs3c6FcNlEgu/Oh+dLv2S6uPXbBoEEP16tWtXLlyyd6upbz8m2m95mKP+tVpQMQll1ziqwH0BmIAAIDwIdiFmMKaRpxq9KtCnH+482rGRo4cGbJQt3v3bje9iR53+/btNnPmTFc7+PLLL6f4NzVq1HBzAb7yyivWpEkTd75z507f7RoB+8gjj7hJjzUQQ89PtZfR3DwNAEAsoI9dNtA8dQpUgf3YVFOn7aGcx+6pp55yAz3UzKu1cVevXu2mKtFqHimpWLGi9e3b18aNG2dXXnmlC4WXXXaZ7/aWLVu6FSQU7q666ir3vHRSTR8AAAifHAl0jvI1J9arV8+tOes/qEDr0yoMaXBA/vz5s1TYmvpEffM2btzo5nXTPHDZ1fwaTN9++61rxtVz8PrfnXfeeW61j6wu4xbM8gYAIJYzSnJois1GCnFq2ox2s2fPtu+//94ee+wx189wypQp7o1Wu3btcB8aAABxjWCHDNOEy48//rhr2j148KDVqVPHXnzxxUQjawEgHsVKywyiF8EOGabauSFDhlByABABa4ID/hg8AQBAjK0JjvhFsAMAIIvNr6qpS2lNcOnVq5fbL9x0DF988YVbG1znkXBMCC6aYtPp2LFjQS56JIdB2gCijfrUBdbUBX6urV271u0XzgF00dhUrO9ehU/NvqDLOqk8A8/Tc/lYBvbNyN9pDleNWNXqUJGAYJeGvHnzWs6cOW3Dhg1WsmRJd52JeEND/yCaTFnly5x4AKKFBkoEc79gUfDYv3+/OynUde/ePck+Cnnt27e3O++8084++2w7dOiQOx0+fDjJ5eS2Bd7uhTCdJ3fK6G3RomnTpvb5559bJCDYpUGhTnOq6R9S4Q6h5S2xxigyANHCm9Mzq/sp2GjVn23btrmVgnbt2uVWD0rrpP00z5lWAPKCnE4KW+mleUhj5TskZ86c7tz/cnLb0nM5pdu966Lz1q1bW6Qg2KWDaunKly/v+zWB0FFNHaEOQDTRlCaprQkuxYoVsyVLlrh5QBXckjv5L90YDlqlSOFT33k66fM48HJy27zLOuXOndt9hid3yuptClDeKbmgRWvacQS7dPKaB2kiBID4ppowhbi//vrLd6patWqq/ewU3Hr37p2u+y9SpIiddNJJ7lynE044IV0n9fEqUKBAktPbb79t119/fZqPe8cdd9i1116bobJA5CHYAQAQ4J9//rHffvst0enPP/90IU5dc9I70EuVAqVLl7YzzzzTTjnlFCtRooQVL17c1eAldzrxxBODXoEQuE55VpuUEdkIdgCAuOSNVl2+fLn98MMPtnLlShfgfv31V9fHLTVaaUdddPxPCnClSpVywU9dd6pUqWKXXHJJ2LuXpNVU7PVt1n6IfgQ7AEBcNJ8qvH333Xe2bNkyF+ZWrFiRar821XRVqlTJdzrjjDN8IU6zJERLny4FS01poomSdcz+4c57DiNHjgx7AEVwEOwAADFFwWXVqlX2zTff2LfffmuLFi2y77//3q1tHUgd88866yyrWbOm1ahRwypXruxOCnGFChWyWKF56mbOnJnsPHYKdZE6jx0yjmAHAIj6IPfTTz/ZvHnzfKdNmzYl2U8DEurXr29169Z1QU4nhTqN6owHCm9t2rRxEyWruVh96tT8Sk1dbCHYAQCijuYV/fjjj91JQW7z5s1J+sBpNYAGDRrYOeec485VCxctzaehohAXztUvEHoEOwBAxNNgBDWtfvjhh/bRRx+5plV/mtbj/PPPd6GlcePGLsjlz58/bMcLhAvBDgAQsWFOC9XPmDHD3nrrLbfkoEc1bxp1etVVV9lll13mauXipUkVSA3BDgAQMbS+qZpWX3/9dRfmtm7d6rutcOHCbvUfLZelfnU///yzTZw40WrXrk2oA/5FsAMAhN3q1att0qRJNnnyZFuzZo1vuyb0bdu2revo//jjjyf5O83Npmk8NOKTkZ0AwQ4AEMa55dTM+uKLL7omV4+W0erYsaNdffXVrs+cml0rVqyY7H2o5k639+rVy434ZIQn4h01dgCAbKWpNsaPH2/PP/+8/f33326bwplWaejataurodNgCI9CX2rrsHorSGgaD0Z8It4R7AAA2WLp0qU2bNgwe+ONN+zw4cNum5bh6t69u914441uRYeUgmB6pHc/IJYR7AAAIfXVV1/ZwIED7YMPPvBtu/DCC+3uu+92/eLSWvQ+vYvTs4g9QLADAISImlA14GHu3Lnues6cOV3fud69e7vJg9OLReyB9MuZgX0BAEhXk2vz5s2tadOmLtSpRu6mm25y05O89tprGQp1/ovYS+DKESxiDyRGsAMABG3Kkuuvv97q1Kljn3zyiQt0d9xxh61atcqNfK1UqVKWF7EvU6ZMou1axJ6pToD/0McOAJAlu3fvtieeeMJGjhzpGxRx7bXXum1anzVYWMQeSBvBDgCQKZpmZPr06a7P3IYNG9y2Zs2a2aBBg6xu3bohKVUWsQdSR7ADAPhoyS7NB6epQzTKVAMXkpv0V/3l1MzqDYw4/fTTbfTo0dayZUtKEwgj+tgBAJxZs2a5FR406KFTp07uXNe13XPkyBEbPHiwW59VoS5//vz22GOP2Y8//kioAyIANXYAABfetOaqmldTWou1SpUqbmWIRYsWudsuu+wyGzdunJ122mmUIBAhCHYAEOfU/NqzZ88koU68bd26dbP9+/e79V2LFi3qBkp06dIlyfQjAMKLYAcAcU596lJbi1V27tzpzlu3bu3WeNVSYAAiD8EOAOJcetdYveWWW2z8+PHU0gERjMETABDn0rvGqgZU0PQKRDaCHQDEOW8t1tRCW7ly5dx+ACIbwQ4A4py3FmtygycU9nTSYInk5rMDEFkIdgAAtxRY3rx5k5QEa7EC0YXBEwAQx1RLN2TIEOvXr5+73rx5c7eixJ49e1JdeQJAZCLYAUCc0ioSd911lxvpKvfcc48NHTqUIAdEsZzRNiS/e/fubnHpiy++2CZNmuS7beXKlXbVVVdZrVq1rH379rZixYqwHisARDLVyLVp08Y3fYn62A0fPpxQB0S5qAp2vXr1soIFC7qlbwYMGOA683722We2b98+u/XWW61+/frutjp16rgAqO0AgMS2bdvmfhx/+OGHbq3XN99803r06EExATEgaoKdZj1funSp3X777W5R6ksvvdT1/fjqq6/ch1O+fPmsT58+dsYZZ9gDDzxghQoVso8//jjchw0AEeXvv/+2Jk2auPVeixcvbnPnzrW2bduG+7AAxFuw06/KAgUKuBo5jd76448/bMmSJVa1alVbtmyZ1atXzzcHk87VXKsgCAA4TsuGNWrUyH744Qc75ZRT7IsvvrDzzjuP4gFiSNQEO9XIPfzwwzZ9+nTXj+7yyy93H1DqV7dlyxYrVapUov31S3TTpk0p3p8WslYfE/8TAMQq/RhWK8evv/7qJhueP3++1ahRI9yHBSCeR8X+/vvv1rRpU+vatav99ttv9sQTT9j5559v+/fvTzL/kq4rvKVEHYbHjBmTDUcNAOG1Zs0a1/y6du1aO/PMM2327NlWoUIFXhYgBkVNsFNfupkzZ9q8efNcs2zNmjVdX5Fx48a5X5+BIU7XtV9KNLhCAdGjGrvGjRuH9DkAQHZbv369GyihUFelShXXpy69a8MCiD5R0xSr6Uv0C9M/rFWrVs02bNhgJ598sm3dujXR/roe2DwbWKNXuHDhRCcAiCWbN292A83UDHv66afbnDlzCHVAjIuaYKeQpuYE/5o5fVhpuRv1ufv+++996xzqXAMrtB0A4nVKE4W6n3/+2bVqKNSVKVMm3IcFIMSiJtipKSFPnjz24IMP2urVq+3zzz+3559/3jp37uyWwNm1a5cNHDjQVq1a5c7V704DLAAg3uzdu9datGjhG/2qUKdpogDEvqgJdieccIJbaUIjYDt06GCDBg1yc9p17NjRNaNqMMTixYutXbt2bvqTCRMmuMmMASDelgm75ppr7JtvvrFixYq5gRKVKlUK92EByCZRM3hCNJpr4sSJyd529tln21tvvZXtxwQAkULdUO688057//33XX/k9957z6pXrx7uwwKQjaKmxg4AkDp1Q1FrhSZpf+211+yCCy6gyIA4Q7ADgBgwefJke+ihh9zlZ599lmXCgDhFsAOAKPfll1/aLbfc4i7369fPNccCiE8EOwCIYn/99Ze1b9/eraGtJRbVHAsgfhHsACBK7du3z6688ko3EXHt2rXd4LKcOflYB+IZnwAAEKUjYLUsoiZnL1mypL3zzjtWqFChcB8WgDAj2AFAFNJcnm+88YabuH3WrFlWvnz5cB8SgAhAsAOAKKOVJLQKj4wdO9YuuuiicB8SgAhBsAOAKLJhwwbr1KmTa4q9+eabfaNhAUAIdgAQZcuFabBErVq1bPTo0eE+JAARhmAHAFFCza8LFixwa2fPmDHDChQoEO5DAhBhCHYAEAW0/uvgwYPd5ZdfftkqVaoU7kMCEIEIdgAQBf3qunTp4i736NHDOnToEO5DAhChCHYAEMGOHTvmQt22bdusbt26NnTo0HAfEoAIRrADgAg2atQomz17tutPN3XqVMubN2+4DwlABCPYAUCEWr58ufXr189dHj58uJ111lnhPiQAEY5gBwAR6MCBA3bdddfZoUOHrHXr1ta9e/dwHxKAKECwA4AI1L9/f1uxYoWVKlXKXnzxRcuRI0e4DwlAFCDYAUCEmTdvno0cOdJdnjhxogt3AJAeBDsAiCD79u2zm266yV3WcmEtWrQI9yEBiCIEOwCIIA888ID9/vvvVrZsWaY2AZBhBDsAiBALFy5005vICy+8YEWLFg33IQGIMgQ7AIgA+/fvt27dullCQoJ17drVmjdvHu5DAhCFCHYAEAEefvhh+/XXX6106dJuzjoAyAyCHQCE2ZIlS3xhbvz48XbiiSeG+5AARCmCHQCE0dGjR93kw1oTtmPHjtaqVSteDwCZRrADgDAaN26cfffdd1akSBEbMWIErwWALCHYAUCYbNiwwQYMGOAuDxo0yE499VReCwBZQrADgDC55557bPfu3dagQQPWggUQFLmDczcAgPT2qVuwYIF9/PHH9sYbb1iuXLncgAmdA0BWEewAIJvMmjXLevbsaevWrfNtK1CggP3xxx9Wu3ZtXgcAWUZTLABkU6jr0KFDolAne/bscdt1OwBkFcEOALKh+VU1dVpVIiW9evVy+wFAVhDsACDE1KcusKbOnwLf2rVr3X4AkBUEOwAIsY0bNwZ1PwBICcEOAEIsvfPTMY8dgKwi2AFAiDVs2DDV9V9z5Mhh5cqVc/sBQFYQ7AAgxHbt2pXiwAiFOhk5ciRz2QHIMoIdAITY448/7laYKF++vJUpUybRbWXLlrWZM2dau3bteB0AZBkTFANACP3+++82duxYd/mll16ypk2butGvGiihPnVqfmXVCQDBQrADgBDq37+/HT582Jo3b26XXnqp29akSRPKHEBI0BQLACHy9ddf24wZMyxnzpw2ZMgQyhlAyBHsACAENOnwfffd5y7feOONVrNmTcoZQMgR7AAgBN5++21buHChFShQwA2eAIDsQLADgCBTn7q+ffu6y717904yEhYAQoVgBwBBNmHCBPvtt9+sVKlS1qdPH8oXQLYh2AFAEGm+uscee8xdfvTRR+2EE06gfAFkG4IdAASRVpDYsmWLVapUyW6++WbKFkC2ItgBQJBs27bNhg0b5i4/8cQTlidPHsoWQLYi2AFAkGiuOq0LW6tWLbvqqqsoVwDZLqqC3aFDh1zflXPOOccuuOACGz58uJsrSlauXOk+SPWB2r59e1uxYkW4DxdAHNESYaNHj3aXn3zySTcpMQBkt6j65NGH5f/+9z+33uIzzzxjb7zxhk2fPt327dtnt956q9WvX99mzZplderUse7du7vtAJAdnnrqKdu/f7+dd9551rJlSwodQFhEzVqxO3bssDfffNMmTpxoZ599ttvWrVs3W7ZsmeXOndvy5cvnphXIkSOHPfDAAzZ//nz7+OOPrV27duE+dAAx7s8//7Tx48f7Ap4+hwAgHKKmxm7x4sVWuHBha9CggW+baukGDRrkwl29evV8H6Y6r1u3ri1dujSMRwwgXmhlCU1KfOmll1rTpk3DfTgA4ljUBLu1a9e62du1TE/z5s3tkksusbFjx9qxY8fc1AKaCNRf8eLFbdOmTan219uzZ0+iEwBk1M8//2yTJ092lwcOHEgBAgirqGmKVX+5NWvW2LRp01wtncLcww8/7NZhVL+WvHnzJtpf1xXeUqJmkzFjxmTDkQOIZfoc0g/MNm3aJGpRAIBwiJpgp350qlXToAlv3cUNGzbY66+/bhUqVEgS4nQ9f/78Kd6fBld07drVd1333bhx4xA+AwCxZvny5TZjxgzX/UPz1gFAuEVNsCtZsqQbIOG/mPZpp53mphjQr+StW7cm2l/XA5tnA2v0Amv5ACCjfevk6quvtpo1a1J4AMIuavrYaX66gwcP2urVq33b/vjjDxf0dNv333/vm9NO50uWLHHbASAUfvjhBzdSX7V1Dz30EIUMIHaC3YcffujWRGzVqpXddNNN7nqwnX766dakSRPr37+/66y8YMECmzBhgl177bVuMIVme1fH5VWrVrlz9bu7/PLLg34cACBe02uHDh2sevXqFAqA2Ah2U6dOtfvuu8+OHj1qVapUcQGrd+/eNmXKFAs2rcFYvnx5F+b69u1r1113nXXu3NlNg6LBEJoSRfPWafoThb6CBQsG/RgA4Mcff7SZM2e6gqC2DkAkyZHgtV9mUosWLeyWW26xtm3bJlohYu7cuTZnzhyLFho8obnwvPnyACAl11xzjVv1RssXegEPACIho+RM73D+wMEJ/g/mP6BBTjnlFOaFAxCTtC61ljP0PhsBIOpGxf7000/2f//3f9alSxfXl84/LV500UWuWbRjx45uUmBNJKzm2WbNmoXyuAEgLNQioYYOtVJ4yxsCQNQ1xX700Uc2cuRI14fu9ttvd/3c8uTJ4yYOVifi9957z44cOeKmENEC2FqvNZqaNGmKBZAWDdyqVq2aC3YaiV+7dm0KDUBEZZQM9bFTcFO/kueee85N/turVy9r3bq1u00zr2/fvt2KFSsWlQtgE+wApOX66693LRJaZULLGwJAVPax81/9QSNRP/vsM/fBpv4lao5YuHCh5cyZ0zXFRmOoA4C0/Prrr26lG6FvHYCYmu5E04j06NHDPv30UzfbupbnuvHGG12nYgCIRU899ZRrmVArRd26dcN9OACQtWCnEHfrrbe6SYhvuOEGe+mll+zEE090S+q8++67rmpQE3VqDjsNoACAWLFmzRrXBCsPPvhguA8HALIW7F555RVXQ6c+dJqEWIMmRowY4UKctyrEmDFj3Affhg0b3Nx2GjkGALFAk6Orj/Ell1zi1qYGgKie7mTy5Mmu07D/L9V58+bZbbfd5mrnypUr57bVqVPH9UGZPXu2C34AEO02b95sL774ors8YMCAcB8OAGQ92P3zzz926qmnJpmEWANq9+7dm2T/Sy+91C6++OL03DUARDRN83TgwAFXU9e0adNwHw4AZD3YNWzY0E1xsmPHDrdW686dO23GjBlWoUIFq1SpUrJ/o1GyABDN9Fk3duxYd7l///6M+gcQG8Fu4MCBNnjwYJsyZYodPHjQcuXKZeeee65rmtVlAIhF48aNc5Oya1LiK664ItyHAwBpytAExdp127ZtbjRsrAU6JigG4G///v1WsWJF18dOP2o7d+5MAQGI+IySrho7jyYf1iTEABDrXn75ZRfq1OXkmmuuCffhAEC60BEOAAIcPnzYhgwZ4i736dPHTfEEANGAYAcAATRt019//WWlSpWyrl27Uj4AogbBDgD8aNmwp59+2l2+9957rUCBApQPgKhBsAMAP++884799NNPVrRoUbv99tspGwBRhWAHAH4j/wcNGuQu33nnnVakSBHKBkBUIdgBwL+++OILW7RokeXPn9969uxJuQCIOhma7sRz9OhRGzNmjM2aNcu2bt3q+qQkNzXKypUrg3GMAJAthg4d6s67devmBk4AQFwEu2effdaef/55K1iwoFWtWtXy5s0b/CMDgGy0YsUK++ijj9yPUg2aAIC4CXbvvvuu1apVyyZOnOjCXUzZu1fVjYm3FSr03+UDB1RlmfLfZ2RflZ33WAcPmh05Epx9NYrPW6v30CFNyhWcffPnN/NWHMnIvtpP+6ckXz6z3Lkzvq/KQGWREv3g8OYfy8i+es302qVE+3k/ZjKyr2q29+8Pzr4qA5WFaPGYffuCs69eM712/v8PwdhX7zH/0aUZ2VfHm9ICOfqf8P8Mysi+Kl+/1oYxgwe783bt2tkZZ5yR8vEBQCRLyIQaNWokTJs2LSGW7N69O6Fy5coJu3Pm1NfCf6eCBRPv2KJF4tsDT/46dEh93z17/tu3S5fU9928+b9977gj9X1Xr/5v3/vuS33fFSv+2/eRR1Lf99tv/9t3yJDU95079799x4xJfd/33/9v34kTU9/3jTf+21eXU9tX9+XRY6S2r47Ro2NPbV89d4/KJLV9VaYelXVq++q18ug1TG1fvQc8em+ktq/eWx6951LbV+9Zf6ntq/8Ff/pfSWnfxo0T71uiRMr71q+feN8KFVLet1q1xPvqekr76n786XEC9tFH4ldffZV4PwCIlIyye3ea+2Zq8MSpp55qO3fuDH7KBIAwGluxop1Xty6vAYColUPpLqN/NH78eJs+fbprkk1rMdqoW2B3/vykz4mm2ONoij2OptiYaordvXu31alSxVbt2nV8+549if/nASBSMsrixWnmrkz1sStdurTrYNy8eXNr3LixlShRwnJ6/bT+pdt79OhhUUcf6Kl9qPt/kaUlI/uqD5TXDyqY+6rPVnoHt4RqXwWh9K61mZF91XfM628XzH0VWNL7xZ6RffU/Eop9FVhCsa9Ewr4Z6cebkX3/DY8vv/iibfRCHQDEY43dWWedlfYd58jhZm+PxTQMIDYcOXLEzjzzTNuyZo356hCpsQMQbzV2U6ZMyeyxAUDEmDlzpq1Zs8YqlChhtnVruA8HALIsU8GuQYMGWX9kAAgjNVZ4ExJ3797dbOBAXg8A8RnsPN999519+OGHtm7dOjdJsUbLqt+dqgsBINKXD1uyZIkVKFDAbrnlFoIdgPgOdk899ZS98sor7levv1dffdWuu+46e/DBB4NxfAAQEsOGDXPnXbt2dQPAACCuV55QP7tzzjnH7r77bqtUqZJbL/bXX3+15557zqZOnWp169a1Fi1aBP+IASCLfvzxR9faoEFe99xzz/ER7HPnZnw0OwDEQrB77bXXrFq1am5Jsdx+U0icf/75LuxdffXVbh+CHYBI9Mwzz7jztm3bulGxTpMm4T0oAAiCTK088csvv1irVq0ShTqPtum2n3/+ORjHBwBBtXHjRtdlRO6//35KF0BMydLgidQc1eLoABBhnn32WTt8+LBdeOGFdt555x3fePiw2YQJxy/femv6J8kGgFiosatSpYp98MEHyYY3bXv//fddvzsAiCRaPmzcuHHu8n333fffDYcOmd111/GTLgNAPAW7Tp06uc7HmiJAU57s2LHDnXT55ptvditOXHPNNcE/WgDIgpdfftl9VumH5xVXXEFZAog5mWqK1QfismXL3OjXr776KtFtmv6kY8eO1q5du2AdIwAEZfmwESNGuMu9e/dOsr41AMR1H7uHHnrILr/8cvvoo49s7dq1LtCVL1/eLrvsMlamABCxy4dpzrobbrgh3IcDAJE3eKJ+/fruBADRsnzYXXfd5VabAIC4DXZqbq1cubIVL17cdz09NK8dAITbvHnz3PJh+fPntzvvvDPchwMA4Q12WnJHv3Zbt27tu64Z29OiQRQAEG5ebR3LhwGIdekKdmq60BQnHv3iTU+wA4CIWz4sOfnymb3//n+XASDWg50/rQ+blgMHDmT+qAAgSIYPH+5bPizF+TW1ik7LlpQ5gKiXqfH+l1xyic2ZMyfF2999911r3LhxVo4LAIK6fFiiCYkBIJ5r7LZt22a///677/r69evthx9+sCJFiiTZ99ixYy70HTx4MLhHCgCZWD7s0KFDdsEFF6Q+mEtLik2devzyddexpBiA2A52efLkcc2vO3fudNfVV2X8+PHulNLUAo0aNQrukQJAOmhZwwULFtjq1atdsJP7778/9T/SMmJdux6/fNVVBDsAsR3sTjjhBBs8eLB9//33LrQp0Kk5Nrn+Krly5XLTorBcD4DsNmvWLOvZs6etW7fOty137tx2WDVyABAH0j1BsfrMef3mNB/U9ddfzzx1ACIq1HXo0MH9+AxcSkzLHOpHJ0sdAoh1mRo88corr4Q91N16663Wr18/3/WVK1faVVddZbVq1bL27dvbihUrwnp8ALK3+VU1dYGhzl+vXr3cfgBg8V5jN2rUKGvevLlvLjtdT4v64fXo0cNC4YMPPnAzyWv6Atm3b58LeppA+emnn7bXX3/dunfvbp999pkVLFgwJMcAIHKoT51/82sgBT6taa39mjRpkq3HBgARF+zGjRtnp59+ui/Y6Xq4gt2OHTtsyJAhVrNmTd82TT6aL18+69Onj3vcBx54wObPn28ff/wxTS9AnExrEsz9ACCmg92UKVPsjDPOSHQ9XDSIo02bNrZ582bftmXLllm9evV8q2HovG7durZ06VKCHRAHTj311KDuBwAxHewaNGiQ6vXs8tVXX9l3331n7733nj366KO+7Vu2bLEzzzwz0b4amfvbb7+leF+a20onz549e0J01ABCrWHDhla2bNkUm2P1Y0+3a79kaRmxN9747zIAxPqo2OT8+eefrh/bX3/95UacVaxY0Zo1a2alS5e2YNOEx4888og9/PDDlj9//kS37d+/3/LmzZtom677B7dAmrJlzJgxQT9OANlPnz/q+6uBU4G8mvyRI0e6/VJcUkzz1wFAvAa7p556yi3Vo5Um/A0bNswttH3TTTdZMCmE1ahRI9lf3OpfFxjidD0wAPrT4Iqu3oSk/9bYsQwaEL00Ul9z1ml6E3+qqVOoY6oTAPEgd2anO1E/O00tonCkmjpZtWqVvfjiiy7cqS9LixYtgjoSduvWrVanTh133Qtyn3zyibVq1crd5k/XS5UqleL9qUYvsJYPQPQaPXq0C3VaPmzgwIFuoIQ+h/RjMMWaOo/C4FtvHb+s0faqwQOAKJQjIbWJn1KgIFW4cGGbOnVqkg9MBa6rr77acubM6SYMDRatT+v/S1zh0VvYe9GiRfbCCy+4UbBqdtFTUpPwbbfdlmzTTHJUY6cBGIsXL3bPDUD02L17t5UrV84te/j222+7AVYZsnevmfd/r/62hQqF5DgBIDMyklEyNUGx+tQp3CX3K1i1YJpf7vfff7dgKlOmjFWoUMF3KlSokDvpsubY27Vrl/uVrlpDnavf3eWXXx7UYwAQmdRSoFBXuXJlN58lAMSrTAW7k08+2Y1ETW2gQ7FixSy7KL1qMISSrPrRaPqTCRMmMDkxEAe0Dqz60Env3r1dawEAxKtMdSS54YYbbMSIEXbppZcmmihYNLu7+uB17tzZQkkrTPg7++yz7S2vjwyAuDFjxgzXiqA+tfpsAoB4lq5gpxUdkqOFtdUxWatS6FeyQp1WfFATqZpCASCU1J926NCh7vLdd9+d6kh4AIgH6Ro8cdZZZ2X8jnPksJ9++smiBYMngOgze/Zs+7//+z/X7UK1dpqYPFMYPAEgRjJKumrs5syZE6xjA4Cg8UbHd+vWLfOhDgBiSO70jkgFgEiyfPlyN4+luoHce++9WbszzWk5ceJ/lwEgloOd1mjVNALeL2JdT+9M8AAQytq6Dh062GmnnZa1O8uTx+zGG4NzYAAQ6cFOq0uog7I3P5Sue+svpiaa+tgBiB7r1q2z119/3TdJOQAgA8HurrvusipVqviu33nnnekKdgAQCqNGjXIr0Wh953POOSfrd6hVbT755Pjlyy5jSTEA8bWkWCxiVCwQHbTChJYP0zJi77//vrVs2TLrd8qoWADxvKQYAISLVpVRqKtWrRrLBgJAMFaekGnTprlfy5s3b7ZDhw4luV1NtXPnzs3s3QNAEvqsUTOssHwYAAQp2GnJsIEDB7rLGimbL1++zNwNAGSIBkysX7/eTj31VLvuuusoPQAIRrB77bXXrGLFivbCCy+4vi4AEGrHjh2zwYMHu8s9e/bkByUABKuPnaYa6NSpE6EOQLZ599133RRKRYsWtdtvv52SB4BgBbsSJUq4xbcBIDvo82bQoEG+6ZaKFClCwQNAsIKdpheYNWuWm0cKAELtiy++sG+//dby58/vmmGDTsuIjRlz/MSSYgBivY/dzJkzE11Xx+W//vrL2rZta82bN7eSJUu69RoDaakfAMiqp59+2p3fdNNNVqpUqeAXqJYUu/PO4N8vAETiBMVnnXWWm77E29X/cpI7/Pc2nUfTkmJMUAxEJk3IWb9+fcuVK5etWrXKDdwCgHiyJwMTFKerxs7r2wIA2c0bCXvttdeGLtQdPWq2YMHxyw0bmuXKFZrHAYAQS1ewU5MrAGS3X3/91dcVpG/fvqF7oAMHzJo2PX55zx6zQoVC91gAEEIsKQYgYg0dOtR17WjdurXVqFEj3IcDALE5QbFGw44dO9aNjN26daubODSQ+titXLkyGMcIIA5phYnJkye7y/369Qv34QBA7Aa7MWPG2PPPP28FCxa0qlWrWl6mBwAQZCNGjLDDhw9bo0aN7IILLqB8ASBUwU4zwNeqVcsmTpzowh0ABNO2bdvcj0fp378/hQsAoexjt2XLFmvXrh2hDkBIjB492vbu3et+QF522WWUMgCEMthpguKdO3dm5k8BIFX6bBk1apS7/MADD7j+ugCAEAa79u3b27Rp09yEeQAQTOrDu2PHDqtWrZr7rMkWWnliyJDjJ10GgHjqY1e6dGn3K1rLiTVu3NhKlCiRZEkx3d6jR49gHSeAOLB7924bPny4r7YuuaUKQ0IDwO6/P3seCwAiLdjd7/cB+Oabbya7D8EOQEaNGzfODZyoXLmydezYkQIEgOwIdlOmTMnMnwFAivbt22fDhg1zlwcMGODWhs02WlJsyZLjl+vWZUkxAPEV7Bo0aBD8IwEQ18aPH+9G3J9++unWqVOn7H1wLSnmfa6xpBiAKMaSYgDCbv/+/TZEAxf+nbcuDwMYACB0NXY33HBDhu9Yfey85YAAIDUvvfSSbdq0ycqXL5+pzxsAQAaC3bfffmsZxdxTANLj4MGD9vTTT/vWhGWJQgAIcbCbM2dOFh4CAFI2adIkW79+vZUpU8a6detGUQFAqIOdPnABINgOHTpkgwYNcpf79Olj+fLlo5ABIAsYPAEgrH3r1qxZ45YpvOWWW3glACAc050AQDBGwj755JO+VSYKFCgQvkLVKNxHHvnvMgBEKYIdgLB4/vnnbcOGDW4k7M033xzeV0FLij36aHiPAQCCgKZYANluz549vpGwDz30EH3rACBIqLEDkO3GjBljmzdvtjPOOMO6dOkS/lfg2DGzn346frlqVbOc/OYFEJ0IdgCy1c6dO32rTDz66KORscrE/v1mNWocv8ySYgCiGD9LAWSrkSNH2vbt261q1ap27bXXUvoAEEQEOwDZZtu2bTZ8+HB3+bHHHrNcuXJR+gAQRAQ7ANlm2LBhtmvXLqtVq5a1b9+ekgeAICPYAcgWGzdutFGjRrnLjz/+uOVkgAIABB3BDkC2UNPrvn377LzzzrPWrVtT6gAQAgQ7ACH3yy+/2Isvvugua0Rsjhw5KHUACAGmOwEQcgMGDLCjR4+6mrqGDRtGXolrypX77vvvMgBEKYIdgJD66quvbNasWa5P3aBBgyKztLWk2NCh4T4KAIivpti///7bevToYQ0aNHC/+vUlcfDgQXfb2rVr7cYbb7TatWtbixYt7Msvvwz34QJxLyEhwfr06ePKQf+f1atXj/syAYBQyhlNXxAKdfv377epU6faiBEjbO7cuW6yU9125513WokSJezNN9+0Nm3a2F133eUWGAcQPu+//777kZU/f343eCJiaUmxP/88ftJlAIhSUdMU+8cff9jSpUtt4cKFLsCJgt7gwYOtUaNGrsZu2rRpVrBgQbf+pJp/FPLuvvvucB86EJeOHDli/fr1c5d79eplZcuWtYilJcVOO+34ZZYUAxDFoqbGrmTJkm5UnRfqPHv27LFly5ZZtWrVXKjz1KtXzwVBAOExadIkW7lypRUrVsz69u3LywAA2SBqgl2RIkUSjaY7duyYvfrqq25OrC1btlipUqUS7V+8eHHbtGlTGI4UgFaXeOCBB1xBPPjgg3biiSdSKACQDaIm2AUaOnSoqw245557XL+7vBrV5kfXDx06lOLf6zbV9vmfAATHwIEDbfPmzVa5cmXX/xUAkD2ipo9dYKibPHmyG0ChL458+fLZjh07kgQ3ddhOyfjx423MmDHZcLRAfPn999/doCZ55plnkvzoAgCETtQFuyeeeMJef/11F+4uu+wyt+3kk0+2VatWJdpv69atSZpn/XXv3t26du3qu64au8aNG4fwyIH4cP/997sfVs2aNbOWLVuG+3AAIK5EVVOsatg08nX48OGJvjBq1aplP/74ox04cMC3bfHixW57SlSLULhw4UQnAFmjKYjeeusty5Url/s/ZekwAMheuaOpeee5556zW2+91Y141YAJjyYsPvXUU61///52xx13uC+X5cuXR+4s90AM0pJhmtZEbrvttuiajDh3brM77vjvMgBEqaj5BJszZ4774hg3bpw7BS4wrtCnUXjt2rWzChUq2NixY6106dJhO14g3rz00kvuB9VJJ50U2ZMRJydfPrOxY8N9FACQZTkStGwDXB871QSqCZdmWSBjtm/fblWqVHE16aNGjXKThwMAsj+jRE2NHYDIpdpyhTpNFH777bdb1NHv261bj1/WJOg5coT7iAAgUwh2ADJEXSIWLFhgGzdudH1bNa3Q888/725Tl4g8efJEX4nu22fmjaJnSTEAUYxgByDdZs2aZT179rR169b5tinIqUdH586dmTIIAMKMYAcg3aGuQ4cOLsT5O3z4sDtv0qQJJQkAYRZV89gBCF/zq2rqUhtr9eijj7r9AADhQ7ADkCb1qfNvfk3O2rVr3X4AgPAh2AFIkwZKBHM/AEBoEOwApEmjX4O5HwAgNBg8ASBNDRs2tLJly9r69euT7WenNWF1u/aLSlpGrEuX/y4DQJSixg5AmnLlyuVWlEgp1MnIkSPdflFJS4pNmnT8pMsAEKUIdgDSpVWrVlauXLkk21VTN3PmTLdOMwAgvGhzAJAugwYNciNfS5YsaS+88ILt27fP9alT82vU1tR5VBOp1SekYEGWFAMQtQh2ANK0bNkyGzhwoLv87LPPWps2bWKr1BTqvIW1WVIMQBSjKRZAqg4cOGDXX3+9W2HiyiuvtKuvvpoSA4AIRbADkKqHH37YVqxYYaVKlbIJEyb4BksAACIPwQ5AiubPn2/Dhg1zl9WvTv3rAACRi2AHIFm7du2yLl26uClOunXrZldccQUlBQARjmAHIFn33HOP/fnnn1axYkUbMWIEpQQAUYBgByCJN954w15++WXXn27y5MlWpEgRSgkAogDTnQBI5I8//rBbbrnFXe7Xr581atQo9ktI8/B16PDfZQCIUgQ7AD6HDh2yjh07uv51F154oT3++OPxUTr585vNmBHuowCALKMpFoCPaui+++47O+mkk+y1116z3Ln57QcA0YRgB8B57733fIMkJk2aZOXLl6dkACDKEOwAuNGvN954oyuJnj17xt/UJnv3Hl8fViddBoAoRbAD4ty+ffusbdu2tm3bNqtfv74NHjw43IcEAMgkgh0QxzT58K233mpLly51q0rMmjXL8uXLF+7DAgBkEsEOiGOjR4+2qVOnWq5cudzcdeXKlQv3IQEAsoBgB8SpuXPnWu/evd3lZ555xpo0aRLuQwIAZBHBDohDq1evtquvvtqOHj1q119/vfXo0SPchwQACAKCHRBntm/fbi1atLCtW7da3bp1bcKECW7pMABA9GP2USDOVpZo3769/fzzz1a2bFk3d12BAgXCfVjhp2XEWrT47zIARCmCHRBHI2C1Bqz61hUuXNg++OADK126dLgPK3KWFPvgg3AfBQBkGU2xQJx44oknbMqUKW4E7IwZM+zss88O9yEBAIKMYAfEgRdeeMEeeeQRd3ns2LHWvHnzcB8SACAECHZAjNP8dN27d3eX+/Xr57sMP1pGrFCh4yeWFAMQxehjB8Swjz76yE1nov51CnRPPfVUuA8pcu3bF+4jAIAso8YOiFELFixwI2APHz5sHTt2dE2wTGsCALGNYAfEoG+++cZatWpl+/fvd3PWeYMmAACxjaZYxB2ttqDarI0bN9qpp55qDRs2jKnQ87///c8Njti9e7c1atTIjYDNmzdvuA8LAJANCHaIK7NmzbKePXvaunXrfNs0Ue+oUaOsXbt2Fu2+/PJLu/zyy23Pnj3WuHFje//9961gwYLhPiwAQDahKRZxFeo6dOiQKNTJ+vXr3XbdHs3mzZvnauoU6i6++GI3AbEmIgYAxA+CHeKm+VU1dRodGsjb1qtXL7dfNPrwww9dTd3evXutWbNmrqaukKbuQPrkzGnWuPHxky4DQJTiEwxxQX3qAmvqAsPd2rVr3X7RZtKkSXbFFVf4Bkq88847rP+aUVov94svjp9YOxdAFCPYIS5ooEQw94sECqNPP/20de3a1dU0du7c2d5++23Lr3VPAQBxiWCHuKDRr8HcL9wU5NR03L9/f3e9T58+ruYuT5484T40AEAYEewQFzSliUa/pjRBr7aXK1fO7Rfpdu3aZW3atLHRo0e76yNGjLDBgwdbTvqGZZ6WEStZ8viJJcUARDGCHeKC5qnTlCYSGO686yNHjoz4+exWrVpl5513nhvxqibXadOmuZo7BMHWrcdPABDFCHaIG5qnbubMmVamTJlE21WTp+2RPo/dnDlzrEGDBvbTTz+556CBHloqDAAADxMUI64ovKkZM5pWnjh27JgNGzbMBgwY4PrWnXvuufbWW29FTX9AAED2Idgh7ijENWnSxKLB1q1brUuXLm6eOrnhhhts/PjxjHwFAMR+U+zBgwddrUb9+vXtoosuspdffjnchwRk2sKFC61OnTou1Kk/3QsvvOBGvjKdCQAgLmrshgwZYitWrLDJkyfbhg0brG/fvla6dGm3zBIQLQ4fPmwDBw60J5980jW9Vq5c2WbMmGFnn312uA8NABDhYibY7du3z335qVajevXq7vTbb7/Z1KlTCXaIGvphoubW77//3l3v1KmTPf/883bCCSeE+9Bim6aKqV//v8sAEKVi5hPs559/tiNHjrimK0+9evVs2bJlrvM5EMlUMzd06FD3nlWoK1asmJvKRD9MCHXZQMuILVp0/MSSYgCiWMzU2G3ZssVOOukky5s3r29biRIlXL+7HTt2uC9KIBJ999131r17d1uyZIm7rvVeX3zxRUa9AgDit8ZOC6D7hzrxrh86dCjJ/tq2Z8+eRCcgO+3cudPuvvtuNzedQl3RokVdV4L333+fUAcAiO8au3z58iUJcN715EYRasqIMWPGZNvxAR51DXjllVfcOq+aS8/rSzd8+HA7+eSTKahw2LfPrFq145dXrjQrWJDXAUBUiplgpy/E7du3u352uXPn9jXPKtQVKVIkyf5q+uratavvumrsGjdunK3HjPjz+eefW+/evW3p0qXueqVKley5556zSy+9NNyHFt8SEszWrPnvMgBEqZhpiq1ataoLdN4XpixevNhq1qyZ7OLoaqYtXLhwohMQKitXrrTWrVvbJZdc4t6j+rExePBgW758OaEOABA0MRPsChQoYFdeeaU9+uij7sty9uzZboJiTR0BhIvWdVUza40aNVzfOa16cdddd9nvv/9uffr0YbJhAEBQxUxTrKjPkoKdlmBSDZw6pjdr1izch4U4raF74oknbPr06Zbwb9Oefng8/fTTVqVKlTSnPommtWwBAJEjpoKdau3UvKUTkN0U4ObPn28jRoywd9991xfo2rZtaw8//LDVrl07zfuYNWuW9ezZ09atW+fbVrZsWRs1apS1a9cupMcPAIh+MdMUC4SLRl9rImGtUdykSRN75513XKhToNNkwwpr6Q11HTp0SBTqZP369W67bgcAIG5q7IDstGrVKnvppZds0qRJtmnTJrdNo7DVFaBXr1521llnpfu+1Pyqmjqvls+ftuXIkcPdZ5s2bWiWDYUcOf6b7kSXASBKEeyADDhw4IC99dZbbiLhuXPn+rafcsopblCEptHRiicZpT51gTV1geFu7dq1bj/VCiLING/djz9SrACiHsEOSIPmRtT8c1q7Vc2hWjFCVIvWvHlzu/nmm91UJnny5Ml0WXoTFQdrPwBAfCLYAck4fPiwffnllzZjxgx32rp1q++2cuXK2U033eQmuC5fvnxQyk+jX4O5HwAgPhHsgH9t27bNPvroIzff3Mcff2w7duzwlU3JkiXtqquusmuuucYuvPDCZCe9zgpNaaLRrxookVw/O9UO6nbthxAtKXbOOccvL1rEkmIAohbBDnFdK7do0SLXzPrpp5/awoUL3TquHvWVUxOrwtzFF1/sW6ouFDRPnaY00ehXhTj/cKfrMnLkSAZOhIrKW2vEepcBIEoR7BBXfeWWLVvmgpwGPmjOub179ybaRytEKMy1atXKzj333GwNUpqnbubMmcnOY6dQxzx2AIC0EOwQszTQ4JtvvrGvv/7anb777rskQa548eLWtGlTd2rRooVVrFjRwknhTVOasPIEACAzCHaIemo+/fPPP11tnHdasmSJ/fXXX0n2LVKkiDVu3Ng1rSrM1axZM+j95bJKtYRMaQIAyAyCHaKGJvFds2aN/fLLL/brr7+68+XLl7vT7t27k+yvvmlqWj3vvPPcSU2rmjSYdVcBALGKYIeIsn//flfTpgCn0x9//OECnE5a6UHLdyUnb968Vr16datVq5bvpCW+TjjhhGx/DgAAhAvBDtlCozz37Nnj+r1p+S2ddFnTe6gZ1Qtyf//9d6r3ky9fPjvzzDOtSpUq7uSFOV3OygTBiHMaeVyhwn+XASBKEeyQ6X5tmudNc7/9888/Sc51UkjzgpzO92musHQoVKiQVahQwQ1k0Kly5cq+IKcJgWlKRUiWFPvzTwoWQNQj2MVRjZmaMdXU6X9S37Rdu3a588DLyd2m5bQU2rZv357sRLppKVy4sFs9QWur6lwnhTcFOe9UrFgx39xtAAAg/Qh22UjNjhs2bHABS5Pjeuf+lzO6TYvSB4a1lE6ZCWLpCWqaMkRhTCfvss5PPvlkX4DTuU7aHwAAhAbBLpssXrzYGjRokGhlg3BRbViBAgXcSdN/aICBTild9r9etGjRREFOgxaAqLd/v1mjRscvz59vVqBAuI8IADKFYJdNVFtVp04d27JliwtD6ugfeJ6Zbfnz5/eFNP9TStt10t/S1An40Q+u77777zIARCmCXTYpU6aMW/kAAAAgVCJryn0AAABkGsEOAAAgRhDsAAAAYgTBDgAAIEYweAIApEQJygFA1CPYAUChQmZbtlAOAKIeTbEAAAAxgho7BNXRo0dtwYIFtnHjRreUWMOGDS1XrlyUMgAA2YAaOwTNrFmzrGLFita0aVPr1KmTO9d1bQcifkmxJk2On3QZAKIUwQ5BofDWoUMHW7duXaLt69evd9sJd4hoWkZs3rzjJ5YUAxDFCHYISvNrz549LSEhIclt3rZevXq5/QAAQOgQ7JBl6lMXWFMXGO7Wrl3r9gMAAKFDsEOWaaBEMPcDAACZQ7BDlmn0azD3AwAAmUOwQ5ZpSpOyZctajhw5kr1d28uVK+f2AwAAoUOwQ5ZpnrpRo0a5y4Hhzrs+cuRI5rNDZCtY8PgJAKIYwQ5B0a5dO5s5c6aVKVMm0XbV5Gm7bgciekmxvXuPn3QZAKIUK08gaBTe2rRpw8oTAACECcEOQW+WbaLZ+wEAQLajKRYADhwwa9ny+EmXASBKUWMHAFoV5cMPj5cDK6QAiGLU2AEAAMQIgh0AAECMINgBAADECIIdAABAjCDYAQAAxAhGxf4rISHBne/ZsyecrweAcNCKEzn//Z2rz4B/Pw8AIBJ42cTLKqkh2P1rrz7Yzaxx48ahfG0ARKozzzx+3qhRuI8EAFLMKieccIKlJkdCeuJfHDh27Jht3rzZChUqlGQh+w4dOrj1TgMltz1wm/91JW4Fx3nz5lnhwoVD9lxSOt5g/m1a+6V2e3rLM63ypTzTV07p2ZZdZZnScQT774L9/ozU//WUji3Yfxcv5Zkdn53p2Tcj3znxXp7p2a9DDJSnoppCXalSpSyn17qQAmrs/qWCOuWUU1K8LbkXK7ntgduS20fXQ/nPlNLxBvNv09ovtdvTW57pKV+hPNNXTunZFuqyTOk4gv13wX5/Rur/ekqPG+y/i5fyzI7PzvTsm5HvnHgvz/TslzNGyjOtmjoPgyfS4brrrkv39sBtKf1tKGXlMdP7t2ntl9rt6S3P9JRvdojG8szKtlDL7GNm5O/CUZ7hKMusPC7lGbyyzK7y5Lsoc+V+XYyUZ3rRFJuNVF1br149W7x4cch/xccDypOyjFS8NynPSMb7M7bLkxq7bJQ3b16766673Dkoz0jCe5PyjGS8PynPSJY3wr7bqbEDAACIEdTYAQAAxAiCHQAAQIwg2AEAAMQI5rGLMIcPH7a+ffvapk2brECBAjZ06FArVqxYuA8rKh08eND69Olj//zzjx06dMgGDBhgtWvXDvdhxYTZs2fbnDlzbNCgQeE+lKibCP2BBx6w1atXu8nQhwwZYsWLFw/3YUU93o/BwWdmbHyfU2MXYT788EM7+eST7bXXXrOWLVvaCy+8EO5DilqaFfz000+3V1991Z5++mlCSJAMHjzYhg0blq41C5HYZ599Zvnz57dp06ZZ+/btbfz48RQR78eIwWdmbHyfU2MXYdq0aePeAKKUX7Ro0XAfUlSXpbc83NGjRy1PnjzhPqSYcPbZZ7vlc95+++1wH0rUWbJkiV144YXucsOGDW3ChAnhPqSox/sxePjMjI3vc4JdmEyfPt1eeeWVRNteeukll+5z585tt956q/3www82ceLEcB1iTJSlbNu2zTXJ6oSsl+nll19u33zzDUWZyYlMvQlM1RSrtR+RNbwfg8d7b/KZGTzh+D4n2IVJx44d3Skl+iW/Zs0a94b45JNPsvXYYqks1ZepR48eds8999j555+f7ccWq+9PZP6L0wtzOk/v2o9AduEzM/iy+/ucPnYRWFOiPmFSsGBBt9AwMmfjxo12++2328CBA+3iiy+mGBF2GryzcOFCd3n+/PlWp06dcB8S4MNnZmx8n5MagkSjLlu1apWoiUojjDQSs379+nbRRRfZyy+/nK5mhS+//NKuv/5669mzpz3xxBMWb4JVls8995zt27fPjUTq3Lmzq7mLV8EqU2StXJs1a2b79++3a665xg2guO222yhS3qcR8/7kMzO45Rm27/MEZNmBAwcS7rzzzoTKlSsnfP31177tjz/+eELr1q0TVqxYkfDpp58m1KlTJ+Gjjz6ixCnLbMX7k3KNBrxPKc9IdiCKvufpY5dFq1atst69eyeZ+kE1RTNmzHDDm6tXr+5Ov/32m02dOtWaN2+e1YeNSZQlZRoteK9SnpGM92d8lydNsVn07bff2rnnnuva0v39/PPPduTIkUR9aOrVq2fLli1zk5SCsswOvD8p12jA+5TyjGTfRtn3PDV2WdSpU6dkt2/ZssVOOukky5s3r29biRIlXHv8jh07WE2CsswWvD8p12jA+5TyjGSdoux7nhq7EFEHaf8XW7zr6oAJyjKceH9SrtGA9ynlGcn2R+j3PMEuRPLly5fkhfWua0khUJbhxPuTco0GvE8pz0iWL0K/5wl2IaIZ+rdv3+7a3/2rbfViFylSJFQPG5MoS8o0WvBepTwjGe/P+ChPgl2IVK1a1S0lsnTpUt+2xYsXW82aNZl0mLIMO96flGs04H1KeUayqhH6PU+wC5ECBQrYlVdeaY8++qgtX77cZs+e7SYuvOGGG0L1kDGLsqRMowXvVcozkvH+jI/yZFRsCPXv39+94F26dHFrRN59991u5nlQlpGA9yflGg14n1Kekax/BH7P59AsxWE9AgAAAAQFTbEAAAAxgmAHAAAQIwh2AAAAMYJgBwAAECMIdgAAADGCYAcAABAjCHYAAAAxgmAHAAAQIwh2AAAAMYJgB2RSv379rEqVKvbNN99EdRlefPHF1qhRoyTb//zzT9/ldevWued63333Zcsx6bGuvfbaoN/vTz/95Jb/ad68udWuXdvq1KljV111lU2ePNkOHTpk8cL/tQ31fYfqtcyoxx9/3O64445M/e2zzz7rnodOU6ZMSfP/KfA5f/755+5/bNeuXZl6fCAjWCsWiHMDBgww/5UF9eVz6623WsWKFe3pp58OyzENGTLEihcvHtT7HDt2rDsVK1bMrrjiCitfvrzt3r3b5s6da0899ZR98skn9sILL1ihQoUslj333HPutGLFiqDer95D3bt3t/3799srr7wS0tcyo7799lubMWOGvf/++1m+r48//jjFRd6XLl1q69evTzbsKRAOGjTInYBQItgBce7SSy9NdH379u32/fffu2AXLm3atAnq/U2dOtVGjx5tl1xyiY0YMcLy5cvnu+2WW26xkSNH2rhx41zIHTVqlMWyBQsW2OHDh4N+v0ePHrV58+ZZgwYNQvpaZtSxY8fs4YcftiuvvNIqVKiQpfvS3y9ZssT+/vtvO/nkk5Pc/uGHH7oQ+88//yS57d5773W1w+3atbNzzjknS8cBpIamWAAxTTWQqjUqVapUklDn6dmzp5122mk2Z86cZGtcEL1UE7t69Wq77rrrsnxfl19+uauZ/PTTT5MNkKrNUzN/cs4++2yrWbOmjR8/PsvHAaSGYAdkEzXT3Hbbba5Go0aNGu4LYMyYMXbw4MEk+7777rvul736gTVs2NCGDRvmmpIC+/Tt2bPH1Ta1bt3a7av7VQ2cmlD37t3r22/WrFnub3W/HTp0cPtddtllrtnMv4+d9mvWrJm7/NZbbyXbh1DHocfTfVx00UX25JNPJnos7a+/U7OXnp/uX19o+ht9IR45csTVjml7rVq1rG3btjZ//vw0+2WpFuSJJ55wf6cvST1PBTY1p6b1xX7gwAHr2LFjsqFOcuTI4Zphv/76aytTpoxvu/5Oz0GvlZ6vXju9hnotk+uD9dtvv7naoQsvvNA9ZzX5vv3220keL73P5Y8//nA1Peeff757fL02er11XIHlpcdV+bZv397d57nnnmt9+vSxzZs3J9pPNU7eZfUTlc6dO7vn+Oabb9oFF1zgXhcdT3rfY3rNq1ev7mv21H3rvZTSa7lp0yZ78MEH3ftO96dzXdd2f95x/fLLL657QL169Vy/yBtvvNGWLVtm6TFx4kSrXLmynXXWWUnuW8em96M/7/2rHwGBVNNWokQJF+ACLV682NXktWzZMsVjadWqlasx/fXXX9N17EBm0BQLZAM10fTu3dv177r++utdc82XX37pAoE+6NV5P3/+/G7fCRMm2DPPPOO+KHv16uW+7F999dUk96kvJPX10ZfENddc4y7ri/ajjz5yX2b6Qh8+fHiiv3nkkUfs//7v/1y40xd2gQIFknxx9e3b1wYPHmz169e3q6++2s444wxfkFCN1sKFC61Tp07uC+6zzz5z/am2bt3qvvz9KYzq/nVcavpTcLrnnntccNi4cWOi7XfddZc7bv9Q5W/Lli0u6G7bts0de9WqVe3nn3+2SZMmuWZj9V/KkydPsn/rBQCFgtSUK1cu0XWF3i5duri/V5BRENDznDZtmqv90fNTDY4/9TFTzaDONRhDr6vKU9v0vDPyXJYvX+4CTOHChd3j6b2jQPn888/bV1995fbzD6p6P7333nvuvaAQq5D6zjvv2Nq1a+311193+yisqX+dBjjosvoZevSaaNtNN93krivEpfc9pveI3jN6rqeffroLv3Xr1k22nH///Xf3fPT+0/urUqVKLrjNnDnTvb9ee+01V3vqUTnpf0bh7/7773cDeVRWKpsvvvjCihYtmuJrquPT69e1a1cLhpw5c7ofRCrPwObYDz74wEqXLp3i8xYFfpk9e7YLm0BIJADIlL59+yZUrlw54euvv051v927dyfUr18/4dxzz03YunVrotuGDh3q7uPZZ5911zdt2pRQo0aNhLZt2yYcPHjQt9+aNWsSzj777ESPN3v2bHf9pZdeSnSfhw4dSmjYsGFC7dq1fdvefPNNt+91112X5PiaNm3q9vf8+eefbl89P8/atWvdNh3b6tWrfdsPHz6ccMkllyRUr1494cCBA26bjk/7nn/++Qm7du3y7Ttx4kS3XY+1d+9e3/ZXXnnFbX/jjTd823T9mmuu8V3v37+/2zZv3rxEx65y0/bPPvssxfK/5ZZb3D6rVq1KyIgxY8a4vxs5cmSi7XqNGjRokFCvXj3f8xs9erTbt1u3bgnHjh3z7fvNN9+47ffee2+Gnovuo2XLlgmNGzdO2L59e6L9VE7ab8KECb5tuq7T4sWLE+17/fXXu+3+r5nKVduS22/GjBmJtmfkPab3gvbVffkLfC1vuOEGt+1///tfov1UHoF/7x3XuHHjki2r6dOnJ6TmnXfecfu99dZbSW7z7lvH7c97/w4fPty3zXt9Fy5cmLBo0SJ3ecqUKb7bjxw54t7vgwcPTvY5++9Xs2bNJGUEBBNNsUCIqYZL/by8mjp/d955p6upU42e90teNT3dunWzvHnz+vZTzYqa9fxpIICajXS//lSrpFqMffv2uX4//s4777wsPRfV4vgPqsidO7drSlPNm2pW/KkJ+YQTTvBdV02ONGnSxAoWLOjb7nVoVw1IctSnSTWDquEInJZFtTZqMvZqQpKTK1cudx5YFmlRc5teG9W++VMtjcpcNamqbfWn5ko163pUNt5rkpHnohosNes2btzYHbfK1js1bdrU1dTpfvyVLVs2SW1R4OOnxatVzMp7LDU6ft2fmrTVvOxP5aHtasoNHHwQ+N73npdqP1OzZs0ad+5fM5lVqvlVDax/c6xqR3XMqTXDeu9F1Ur/9ddfQTseIBBNsUCIeR/iZ555ZpLb1FSpJkBvH3XyFv+mKI+auwIp/KnP26JFi9x9qJlq586dvnChL101H3nUfJoVJUuWTLLNa0IOnAcu8LEUApPb7gUv/ylX/O3YscMF48AgIGqmrFatWqrH7DWXKQSo2S+9VJ56bbzn58+7H5W3v8Dn5oVzL/yk97mob52o2Ven5AQO8kjutfEeXyNW0yO590dG32Op0d/qdU7pddB2BTvt5/8jKK1yTYn3Y8P/B0ZaUnofevS81e9P3SPU1KuQpx9m+sHj9TNMTZEiRZK8b4BgItgBIZbWF4W+dL0vKi8c+dfWeQIDhvpOqa+SvrxU06GT+r6pVk196b777rsk9+GFqMxK7xe4f5AL5F+jlR5e5/aM/p1H5aI+USqPwBopf2+88YarBVNtqYJXaq+bFygCX6e0yie9z8V7bPVrU5+u9JRvZssntePPzHssq/8LmSnXlHh/l1qwVW2zf1mmZyoY9a1UH0cNVlF/RtW0p3fUrY4ls88HSA+CHRBiXjPQqlWrktymDvqqefGaI72aOtXYBNZqeLU4HnWiV/OlBluoyc5fWk1U0USDBtR069Vm+lPg0EoSCj8pNYOpiU81NhrxqTnrAgeMeF+2mutOgxjUPO69bgo2GjgSGKrVTCrqLB+K56JmVS8IBYZRhUqN9A0c7BEKwX6PecfslV8g/Y8ooCY3R1xmeLWYmpsxJSp3/0E7yc1BF0gjc0899VT3Oui1Uk1sWs2w/o+XXO0qECz8bABCTH2m1MymppvALw1N+6HpTrxaGU1nodoD7etfc6AmH4149Od9WQWOrtOXjde3KL1NcP682oSM9kkLFdUyaloQ9TvTaFB/Gkmp55tabZXKvkePHm4qDU3/ETi9jJ6nVgNQqNPjqDZK9Joo1AXOO6ZAo5GbWqFC072E4rmoD5nChka1BobA6dOnu9HSCqqZkZE+hxl5j6XnfhVsvX50gc9fo3pVA6jbtV8weAF5w4YNKe4T2FcxualMUmqO1fGqNlhTqSTXVSKQauT1/vGOCwgFauyALNK0D5rqIDma3uOkk05yzVaaCkKdwNV0o/5DGlSh6R3UL+fmm292++vL/Pbbb3fToGjuL817pQ7qChI6Fy/EqGO7/l7ze2lGezVfqR+U+vuohkmhRP25Mlo7oC9VhTt9+ap5MrWBCdlFa9Sq071q3NQ8qdrMH3/80YUbDdJIqbnSo87/6tek6Uc03Yu3pJgCs5rTFLQ0d5v/ck+a9kPLjWl6ENUwqXlWwVx93jRwQlOD+A8CCeZzUUjS/IAauKEpUbSfanV/+OEHt5+OPbPrnnp917QSh0JUas3TGXmP6X2p944Cst6vmi4nuSk99L+g5lw9f/0vqO+pylfvtRNPPNHdHix676osNXef5vdLjqatUa2kN0G1/i/To0WLFu5/X1OuaCqj9NAybgp3ya3NDAQLwQ7IIn35p0RfiAp2ChJqulGTlvrm6MNdX86qeVGfLv/5yDSnmzqLq9ZOXzr6e30pqaZJXyRe/yNt0xermhC1n2qQdJ9a7NxbRkmjNjVnWkbofhQ+NL+cJtFV86Amuw0nlZ0CjQKvalRUa6VaD4UbBbC0+g4qqGq5MI0oVTBTjZNCnbYrfKisFDL8+1optOk10Gumedv0Ba4mXY2KVBD3avZC9VwUuBR2VKuryX4VJk855RQXihT4Mtucpznm1Kz/4osvujneUgt2GX2PacJjzcGotXf1OMkFOwU5PR+t26vXQc9fz0UBVj9qgtUMK/rf0UjhwEm2/SlIek3OGrmtwK4fZGnRJNB63fSDQSEvPbzjUK0tECo5NOdJyO4dQIaoVk5NW8mN4nvooYfcF71qFWjKAdJHtYsKagrp/mu0asJp1UqrtjSlgT7B5vWffOmll7Ll8RCf6GMHRBA1SakJS8tY+VNtjWoGVbOR0uoMAJJSXzjVEqpmMJzUp1ArfqhGHgglmmKBCKJO81qnUk1DGj2n5aY04k5NV+rfpWauYExrAcQLrxle3SLUPJzcfJLZQU3v6jOrEbVAKBHsgAii/lVaB1P9nzQ3lkZKanoO9eeJhL5uQDTSIAr14VP/OfWZzG4aeaupc9RfEgg1+tgBAADECPrYAQAAxAiCHQAAQIwg2AEAAMQIgh0AAECMINgBAADECIIdAABAjCDYAQAAxAiCHQAAQIwg2AEAAFhs+H+bym/qxgZmnwAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "source": [
    "The concentration for the at the Absolute IC50 will be 585.47 nM in this case. Again, it can be double-checked using the AATBioquest IC50 calculator. When searching for the concentration at 50 on the Y-axis, we see X-axis value of 584.72, similar to our calculated Absolute IC50 table."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "fbe7297ab5798b60"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Why go through all of this?\n",
    "IC50 has a lot of variations. There are a lot of factors that can influence the final results, such as experimental designs, or even different laboratory conditions. While two data concentrations are needed to calculate IC50, more datapoints would give a more accurate result. It is always recommended to have additional datapoints to get a more representative dose-response curve. **py50, for example, requires at least 4 datapoints to calculate the IC50**. Typically, the more concentrations tested the better and more accurate the calculated IC50 will be. We can see another example here, where we tested a drug at five different concentrations. \n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "198dd981fddfc325"
  },
  {
   "cell_type": "code",
   "source": [
    "confusing_example_df = pd.read_csv('../dataset/absolute_example.csv')\n",
    "confusing_example = Calculator(confusing_example_df)\n",
    "confusing_example.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:09:56.764336Z",
     "start_time": "2026-05-21T04:09:56.747020Z"
    }
   },
   "id": "f868054a86779e83",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Compound Name  Compound Conc  % Inhibition 1  % Inhibition 2  \\\n",
       "0  Say Wha Drug?          10000              70              71   \n",
       "1  Say Wha Drug?           3000              61              59   \n",
       "2  Say Wha Drug?           1000              42              44   \n",
       "3  Say Wha Drug?            300              25              24   \n",
       "4  Say Wha Drug?            100               9              10   \n",
       "\n",
       "   % Inhibition Avg  \n",
       "0                70  \n",
       "1                60  \n",
       "2                43  \n",
       "3                24  \n",
       "4                10  "
      ],
      "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></th>\n",
       "      <th>Compound Name</th>\n",
       "      <th>Compound Conc</th>\n",
       "      <th>% Inhibition 1</th>\n",
       "      <th>% Inhibition 2</th>\n",
       "      <th>% Inhibition Avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Say Wha Drug?</td>\n",
       "      <td>10000</td>\n",
       "      <td>70</td>\n",
       "      <td>71</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Say Wha Drug?</td>\n",
       "      <td>3000</td>\n",
       "      <td>61</td>\n",
       "      <td>59</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Say Wha Drug?</td>\n",
       "      <td>1000</td>\n",
       "      <td>42</td>\n",
       "      <td>44</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Say Wha Drug?</td>\n",
       "      <td>300</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Say Wha Drug?</td>\n",
       "      <td>100</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "source": [
    "confusing_absolute = confusing_example.calculate_absolute_ic50(name_col='Compound Name',\n",
    "                                                               concentration_col='Compound Conc',\n",
    "                                                               response_col='% Inhibition Avg')\n",
    "confusing_absolute"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:09:56.826244Z",
     "start_time": "2026-05-21T04:09:56.797651Z"
    }
   },
   "id": "37d4e45a18fe379c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   compound_name    maximum   minimum  relative ic50 (nM)  absolute ic50 (nM)  \\\n",
       "0  Say Wha Drug?  78.113661 -3.368617          694.869264         1498.098814   \n",
       "\n",
       "   hill_slope  \n",
       "0    0.834367  "
      ],
      "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></th>\n",
       "      <th>compound_name</th>\n",
       "      <th>maximum</th>\n",
       "      <th>minimum</th>\n",
       "      <th>relative ic50 (nM)</th>\n",
       "      <th>absolute ic50 (nM)</th>\n",
       "      <th>hill_slope</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Say Wha Drug?</td>\n",
       "      <td>78.113661</td>\n",
       "      <td>-3.368617</td>\n",
       "      <td>694.869264</td>\n",
       "      <td>1498.098814</td>\n",
       "      <td>0.834367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "source": "Notice that with this example, the Relative IC50 and the Absolute IC50 values vary greatly, with the ***Relative being in the nM*** and ***Absolute being in the µM*** range. A huge difference! We also see similar calculations when using hte AATBioquest IC50 calculator. When we check the values on a graph, we can see some discouraging results. ",
   "metadata": {
    "collapsed": false
   },
   "id": "dd9b16a3ca7da243"
  },
  {
   "cell_type": "code",
   "source": [
    "test = PlotCurve(confusing_example_df)\n",
    "figure = test.curve_plot(concentration_col='Compound Conc',\n",
    "                         response_col='% Inhibition Avg',\n",
    "                         title='Confusing Drug',\n",
    "                         name_col='Compound Name',\n",
    "                         xlabel='Logarithmic Concentration (µM)',\n",
    "                         ylabel='Inhibition %',\n",
    "                         legend=True,\n",
    "                         conc_unit='µM',\n",
    "                         xscale_ticks=(-2.5, 2.5),\n",
    "                         line_color='black',\n",
    "                         box=True,\n",
    "                         box_color='red',\n",
    "                         conc_target=0.69487,\n",
    "                         figsize=(6.4, 4.8),\n",
    "                         verbose=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:09:57.445489Z",
     "start_time": "2026-05-21T04:09:57.283974Z"
    }
   },
   "id": "84bac0ac66b71584",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compound Name concentration will be in µM!\n",
      "Concentration on X-axis will be in µM\n",
      "Box X intersection:  0.695 µM\n",
      "Box Y intersection:  37.372 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "source": [
    "More specifically, the **Relative IC50** for the Say Wha Drug? is closer to 37.3%. As personal preference, I do not think this is indicative of the meaning for \"IC50\". When the box is plotted for the **Absolute IC50** below, we see a more reasonable graph representation. More specifically, if we look at the data table above, the **Absolute IC50** would make more sense in relation to the data as the 50% inhibition would be located somewhere between 3000 and 1000 nM (See Table in cell 7).\n",
    "\n",
    "Adjusting the box highlight to the 50% response mark provides a more reasonable meaning of IC50 (i.e., highlighting the 50% mark)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "edb2db1eaa60af33"
  },
  {
   "cell_type": "code",
   "source": [
    "figure = test.curve_plot(concentration_col='Compound Conc',\n",
    "                         response_col='% Inhibition Avg',\n",
    "                         title='Confusing Drug',\n",
    "                         name_col='Compound Name',\n",
    "                         xlabel='Logarithmic Concentration (µM)',\n",
    "                         ylabel='Inhibition %',\n",
    "                         legend=True,\n",
    "                         conc_unit='µM',\n",
    "                         xscale_ticks=(-2.5, 2.5),\n",
    "                         line_color='black',\n",
    "                         box=True,\n",
    "                         box_color='red',\n",
    "                         conc_target=1.49940,\n",
    "                         figsize=(6.4, 4.8),\n",
    "                         verbose=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:09:57.617266Z",
     "start_time": "2026-05-21T04:09:57.461261Z"
    }
   },
   "id": "19be5a32baccc76d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compound Name concentration will be in µM!\n",
      "Concentration on X-axis will be in µM\n",
      "Box X intersection:  1.499 µM\n",
      "Box Y intersection:  50.013 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "source": [
    "### How do we fix this?\n",
    "The easiest fix would be to increase the number of concentrations tested for the drug. More points, especially at both ends of the plateau, would help give a more accurate picture of the IC50 values. Here we will add 2 additional concentration points, one at each end of the plateau, to our test and calculate the **Relative** and **Absolute IC50 Values**.\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "263b2d3159ea25c2"
  },
  {
   "cell_type": "code",
   "source": [
    "new_conc = [{'Compound Name': 'Say Wha Drug?', 'Compound Conc': 10, '% Inhibition Avg': 8},\n",
    "            {'Compound Name': 'Say Wha Drug?', 'Compound Conc': 100000, '% Inhibition Avg': 90}]\n",
    "\n",
    "confusing_example_df = pd.concat([confusing_example_df, pd.DataFrame(new_conc)], ignore_index=True)\n",
    "confusing_example_df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:09:57.660710Z",
     "start_time": "2026-05-21T04:09:57.628942Z"
    }
   },
   "id": "44573a82754230a7",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Compound Name  Compound Conc  % Inhibition 1  % Inhibition 2  \\\n",
       "0  Say Wha Drug?          10000            70.0            71.0   \n",
       "1  Say Wha Drug?           3000            61.0            59.0   \n",
       "2  Say Wha Drug?           1000            42.0            44.0   \n",
       "3  Say Wha Drug?            300            25.0            24.0   \n",
       "4  Say Wha Drug?            100             9.0            10.0   \n",
       "5  Say Wha Drug?             10             NaN             NaN   \n",
       "6  Say Wha Drug?         100000             NaN             NaN   \n",
       "\n",
       "   % Inhibition Avg  \n",
       "0                70  \n",
       "1                60  \n",
       "2                43  \n",
       "3                24  \n",
       "4                10  \n",
       "5                 8  \n",
       "6                90  "
      ],
      "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></th>\n",
       "      <th>Compound Name</th>\n",
       "      <th>Compound Conc</th>\n",
       "      <th>% Inhibition 1</th>\n",
       "      <th>% Inhibition 2</th>\n",
       "      <th>% Inhibition Avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Say Wha Drug?</td>\n",
       "      <td>10000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Say Wha Drug?</td>\n",
       "      <td>3000</td>\n",
       "      <td>61.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Say Wha Drug?</td>\n",
       "      <td>1000</td>\n",
       "      <td>42.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Say Wha Drug?</td>\n",
       "      <td>300</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Say Wha Drug?</td>\n",
       "      <td>100</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Say Wha Drug?</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Say Wha Drug?</td>\n",
       "      <td>100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "source": [
    "example = Calculator(confusing_example_df)\n",
    "example_ic50 = example.calculate_absolute_ic50(name_col='Compound Name', concentration_col='Compound Conc',\n",
    "                                               response_col='% Inhibition Avg')\n",
    "example_ic50"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:09:57.774591Z",
     "start_time": "2026-05-21T04:09:57.747474Z"
    }
   },
   "id": "eb99727026a840f5",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   compound_name    maximum   minimum  relative ic50 (nM)  absolute ic50 (nM)  \\\n",
       "0  Say Wha Drug?  92.497685  3.325481         1563.727526         1779.968788   \n",
       "\n",
       "   hill_slope  \n",
       "0    0.723892  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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></th>\n",
       "      <th>compound_name</th>\n",
       "      <th>maximum</th>\n",
       "      <th>minimum</th>\n",
       "      <th>relative ic50 (nM)</th>\n",
       "      <th>absolute ic50 (nM)</th>\n",
       "      <th>hill_slope</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Say Wha Drug?</td>\n",
       "      <td>92.497685</td>\n",
       "      <td>3.325481</td>\n",
       "      <td>1563.727526</td>\n",
       "      <td>1779.968788</td>\n",
       "      <td>0.723892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "source": [
    "From the new table, you can see that adding more points will adjust both the **Relative** and **Absolute IC50** values. But if we were to increase the number of concentrations tested, the two values will begin to move closer to each other. Again, this will appear to be reasonable due to the Inhibition average of the dataset, where 50% inhibition should fall somewhere between the concentration of 1000 and 3000 nM. This will also change the way the graph looks as follows: "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "184d7b340c64dbd4"
  },
  {
   "cell_type": "code",
   "source": [
    "example_plot = PlotCurve(confusing_example_df)\n",
    "figure = example_plot.curve_plot(concentration_col='Compound Conc',\n",
    "                                 response_col='% Inhibition Avg',\n",
    "                                 title='Confusing Drug with More Data Points',\n",
    "                                 name_col='Compound Name',\n",
    "                                 xlabel='Logarithmic Concentration (µM)',\n",
    "                                 ylabel='Inhibition %',\n",
    "                                 legend=False,\n",
    "                                 conc_unit='µM',\n",
    "                                 xscale_ticks=(-2.5, 2.5),\n",
    "                                 line_color='black',\n",
    "                                 box=True,\n",
    "                                 conc_target=1.563735262,  # Highlight Relative IC50\n",
    "                                 box_color='red',\n",
    "                                 figsize=(6.4, 4.8),\n",
    "                                 verbose=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:09:58.336675Z",
     "start_time": "2026-05-21T04:09:58.185497Z"
    }
   },
   "id": "cf4317cd11df3833",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compound Name concentration will be in µM!\n",
      "Concentration on X-axis will be in µM\n",
      "Box X intersection:  1.564 µM\n",
      "Box Y intersection:  47.911 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "source": [
    "If you are feeling adventurous, this can also be calculated using the AATBioquest IC50 calculator to double-check the above results. Both the **Relative** (1563.74 nM) and **Absolute IC50** (1782.84 nM) values are closer to the 50% response, with the Relative IC50 increasing to 47.89% response for this example. This would appear to be more reasonable to the \"50 in IC50\". These results are also reasonable due to the dataset on hand.  "
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    "## Takeaway\n",
    "IC50 values can be confusing. In general, the more concentration points to obtain the plateau for 0% and 100% responses, the better. However, assays can vary by protocols and between labs. The best thing to do is to remember to take stock of your final dataset. Know where the 50% response should lay within your dataset and to make sure that the final calculated (Relative or Absolute) IC50 value makes sense for the data on hand. Keep in mind that the calculated value is representative of the dataset on hand and not the definite result. \n",
    " \n",
    "🍾**Remember: Use your best judgement.**🎉"
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