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<span style="font-size:12.0pt; font-family:"Times New Roman",serif"><img width="600" height="171" id="x_Picture_x0020_3" alt="Dissertation Defense Announcement at the Cullen College of Engineering" style="width:6.25in; height:1.7812in" data-outlook-trace="F:1|T:1" src="cid:image005.jpg@01D845D5.17B10F60"></span><span style="font-size:12.0pt; font-family:"Times New Roman",serif"></span></p>
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<b><span style="font-size:16.0pt; font-family:"Times New Roman",serif; color:red; text-transform:uppercase">Personalized Drug Combinations to Combat Multi-Drug Resistant Bacteria: A Model Based Approach</span></b></p>
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<b><span style="font-size:16.0pt; font-family:"Times New Roman",serif; color:red; text-transform:uppercase"> </span></b></p>
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<b><span style="font-size:13.5pt; font-family:"Times New Roman",serif">Iordanis Kesisoglou</span></b><span style="font-size:13.5pt; font-family:"Times New Roman",serif"></span></p>
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<span style="font-family:"Times New Roman",serif">May 4, 2022; 8:00 – 10:00 AM (CST)</span></p>
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<span style="font-family:"Times New Roman",serif"><br>
Zoom: <a href="https://urldefense.com/v3/__https:/us04web.zoom.us/j/8591721796?pwd=bjdyRnNjMzV4Qm5CYXVzVDFmdzFzZz09__;!!LkSTlj0I!Gptl11ov5B4e7-TC4gzx3wqfmPf02QHSlr4WXyuB7OrHKhxtZPjTR4yPTAkrv7gDJU02L51O-Xkb1UMNZ3nd1Q$" data-auth="NotApplicable">
https://us04web.zoom.us/j/8591721796?pwd=bjdyRnNjMzV4Qm5CYXVzVDFmdzFzZz09</a></span></p>
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<span style="font-family:"Times New Roman",serif">Meeting ID: 859 172 1796<br>
Passcode: v3k6t8</span></p>
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<b><span style="font-family:"Times New Roman",serif">Committee Chair:</span></b><span style="font-family:"Times New Roman",serif"><br>
Michael Nikolaou, Ph.D.</span></p>
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<b><span style="font-family:"Times New Roman",serif">Committee Members:</span></b><span style="font-size:10.0pt; font-family:"Times New Roman",serif"><br>
Vincent H. Tam, Ph.D. <b>| </b>Navin Varadarajan, Ph.D. <b>|</b> Mehmet Orman, Ph.D.
<b>|</b> Elebeoba E. May, PhD.</span><span style="font-size:10.5pt; font-family:"Times New Roman",serif"></span></p>
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<b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:#C8102E">Abstract</span></b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:#C8102E"></span></p>
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<span style="font-size:12.0pt; font-family:"Times New Roman",serif"> Multi-drug resistant bacteria found mainly in hospitals, environments with continuous drug treatment, named superbugs, pose a critical threat for human health. Infections by such
bacteria require a combination of drugs for successful treatment. However, the combined effect of multiple drugs (combination therapy) is difficult to quantify a priori, due to interactions of drugs with one another. Therefore, it is vital to assess the
combined effect of multiple drugs on infecting bacteria in the lab (vitro) prior to live organisms (vivo). Standard in vitro assessment typically involves combinations of up to two drugs in time kill experiments, where the size of a bacterial population exposed
to drugs is assessed at distinct points in time using plating methods. While this approach is sensible, it has two shortcomings: (a) the time-resolution is poor, as the plating process is laborious and samples for plating are typically drawn from a bacterial
population a few hours apart from each other. (b) in experiments where more than two drugs with different half-lives are to be inserted in a human patient, they must be tested in vitro in a continuous dilution system that simulates the PK and PD of an organism.
Yet, the so far experimental designs are limited to two drugs. The overarching goal of the proposed research is to develop a method for drug personalization, namely selection of the kind of drug and dosing regimen to combat multi drug resistant bacteria.
This goal entails the following objectives: (a) Develop and test the mathematical modeling tools needed for analysis of time growth and time kill experimental data from an optical instrument capable of producing continuous measurements in real time. (b) Develop
and test a general methodology for design of experiments that test multiple drugs with different half-lives in a hollow-fiber system that can simulate human pharmacokinetics in vitro. (c) Analyze importance of Lambert function as it appears in part (b). (d)
Use optical experimental data to determine the minimum dosing concentration to eliminate bacteria re-growth, given the Half –life and dosing interval of an antimicrobial agent. The mathematical tools developed are validated through experimentally produced
data in vitro.</span></p>
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<b><span style="font-size:12.0pt; font-family:"Times New Roman",serif">Keywords: </span>
</b><span style="font-size:12.0pt; font-family:"Times New Roman",serif">Mathematical Modeling, Automating process, Design of experiments, In-vitro model, drug with different half-lives</span></p>
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<span style="font-size:12.0pt; font-family:"Times New Roman",serif"><img border="0" width="600" height="82" id="x_Picture_x0020_4" alt="Engineered For What's Next" style="width:6.25in; height:.8541in" data-outlook-trace="F:1|T:1" src="cid:image006.jpg@01D845D5.17B10F60"></span><span style="font-size:12.0pt; font-family:"Times New Roman",serif"></span></p>
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