Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Data-Driven HTS Strategies for Selection of Drug Combinations and 3D Models for Physiologically Relevant Drug Discovery

Kochanek, Stanton (2019) Data-Driven HTS Strategies for Selection of Drug Combinations and 3D Models for Physiologically Relevant Drug Discovery. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

Download (10MB) | Preview


Currently, the approval rate for cancer drugs is dismal, where only ~ 5% of candidates that enter phase I clinical trials become therapies. To address this, it is necessary to improve our preclinical strategies. In particular, the leading clinical observation for patient treatment is that drug combinations more consistently provide better therapeutic outcomes and reduce or delay the emergence of drug resistance as opposed to monotherapy alone. What’s more, models that better recapitulate tumor biology are more likely to be predictive of therapeutic success. Therefore, it was necessary for our laboratory to use data-driven high-throughput / content screening strategies to confirm synergistic drug-drug interactions and optimize cell culture conditions in 3D for drug discovery, to address these preclinical limitations. Specifically, we developed a strategy to confirm and evaluate the synergistic interaction between DCs identified in a pilot screen of 20 drugs performed in pairwise combinations. We were able to both confirm synergism across 4 DCs and develop a mechanism of synergistic action. We also characterized 11 head and neck squamous cell carcinoma cell lines as multicellular tumor spheroids (MCTSs) looking at changes in cellular viability and spheroid diameter over time as well as other microenvironmental characteristics of a solid tumor. Lastly, we applied our MCTSs to screen 19 FDA approved drugs to determine drug sensitivity in both 2D and 3D models. We observed that 2D was consistently more sensitive than 3D and that it was necessary to implement several metrics to adequately evaluate drug effect in 3D.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Kochanek, Stantonstko@pitt.edustko@pitt.edu0000-0001-9954-7767
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairJohnston,
Committee MemberBeumer,
Committee MemberEmpey,
Committee MemberGold,
Committee MemberPoloyac,
Committee MemberJackson,
Date: 5 August 2019
Date Type: Publication
Defense Date: 8 July 2019
Approval Date: 5 August 2019
Submission Date: 1 August 2019
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 228
Institution: University of Pittsburgh
Schools and Programs: School of Pharmacy > Pharmaceutical Sciences
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Drug Combinations, Multicellular Tumor Spheroids, High Throughput Screening, High Content Screening
Date Deposited: 05 Aug 2019 13:05
Last Modified: 05 Aug 2019 13:05


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item