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Albert R. Cunningham, Ph.D.

by Wittliff,James L. last modified Jan 11, 2011 02:25 PM

Associate Professor of Medicine & Brown Cancer Center

CunninghamEmail Albert Cunningham

Phone Number: 502-852-3346 (Office)
Address:
James Graham Brown Cancer Center
529 South Jackson Street
Louisville, KY 40202
Ph. D. 1998, University of Pittsburgh

 

 

Additional Appointments:

  • Associate Professor of Medicine, James Graham Brown Cancer Center, School of Medicine

 

Investigators:

  • Shahid Qamar - Research Associate
  • Alex Carrasquer - Post doc
  • Huihui Wo - Graduate student
  • Naureen Malik - Undergraduate researcher
  • Chris George - High school researcher
  • Sophia Mahmood - High school researcher


Research Interests:

Our individual and public health and our economic well-being are dependent upon the extraordinary properties of chemicals.  However, while many synthetic and natural chemicals have been beneficial to society, some have proven to be harmful to humans and the environment.

From a toxicological point-of-view, the majority of chemicals in use today only have basic toxicological information associated with them and only a few have been adequately studied for their involvement in complex human and environmental health concerns such as cancer, birth defects, and ecosystem damage.  Due to time and cost constraints, accepted methods of toxicity testing cannot adequately assess the existing tens of thousands of chemicals for adverse human and environmental effects.

From a pharmacological point-of-view, many chemicals have therapeutic activity but also have deleterious activity.  The identification of novel compounds and the subsequent synthesis of many congeners is often required to maximize efficacy and minimize toxicity.  However, in many instances even though the therapeutic target is known, the toxicological targets are not.  Thus, even at the cost of excessive safety testing, the toxic effects of some drugs are only realized after widespread use.

My doctoral training and subsequent appointment as an assistant professor at the University of Pittsburgh’s Graduate School of Public Health facilitated a concurrent focus on environmental health and toxicology, pharmacology and drug discovery.  I continue to simultaneously explore the toxicological and pharmacological activity of chemicals through computational structure-activity relationship (SAR) modeling.  Specifically, I focus on chemical carcinogenesis, breast cancer, endocrine disrupting chemicals, and anticancer agents.

My recent work entails the development of predictive and mechanistically-insightful SAR models for chemicals that induce breast-specific tumors.  Breast cancer-specific SAR modeling of chemical carcinogens offers the prospect of reducing the signal-to-noise ratio obscuring our complete understanding of chemical carcinogenesis by isolating or limiting the number of mechanisms under study.  Our latest manuscript describing this approach will soon appear in Chemical Research in Toxicology.  The models “controlled” for mutagenesis (or basal mechanisms of carcinogens) and focused on the question “why do some carcinogens cause breast cancer” which is a different question than “why do some chemicals cause cancer?”

The further combination of my interests in computational toxicology and pharmacology, and the causes and remedies of breast cancer, has allowed me to successfully compete for a second Department of Defense Breast Cancer Research Program IDEA award (July 1, 2005-June 30, 2008, $372,542).  This project is exploring a novel method of exploiting cytostatic and cytotoxic activity across multiple tumor cell lines in order to identify antibreast cancer pharmacophores or “chemical scaffolds”.  It is hypothesized that drugs based on these pharmacophores will be highly specific to (certain types of) breast cancer cells.

My work with breast carcinogens led me to appreciate the fact that I had to compare and contrast the structures of breast carcinogens to those of non­breast carcinogens, not to noncarcinogens, if I wanted to learn something about breast carcinogens.  Similarly, my work with the DTP dataset is exploiting the differences in bioactivity of chemicals between cell lines, not the potency of the chemicals within a single cell line.  Conceptually speaking, the bioactivity term in my models is not the traditional activity value (e.g. potency) but rather a new value, difference, which allows one to look into the structural differences of chemicals associated with contrasting biological endpoints.

We will use computational techniques to compare and contrast the cytostatic and cytotoxic activity of approximately 43,000 chemicals assayed in the National Cancer Institute’s Developmental Therapeutics Program (DTP) database.  Specifically, we will address the question of why some chemicals are more cytotoxic or cytostatic to some breast cancer cell lines and not others (other tumors or other breast cell lines?).  One hypothesis is that chemicals that are significantly more toxic to estrogen receptor (ER) positive breast cancer cells than other cell types gained this “extra” toxicity by interfering with the components of estrogen signaling.  This is different than identifying ER ligands (e.g., SERMs) that directly interact with the ER.  Collaboration with Professor Billy Day at the University of Pittsburgh’s School of Pharmacy, using comparative proteomics analyses, will facilitate the identification of these molecular targets.

We are also refining a new computational tool for analyzing the biological activity of chemicals.  The algorithm is for an expert system called categorical-SAR (cat-SAR).  It is a methodologically-open and information-rich SAR modeling program that associates chemical structures (e.g., 2-dimensional molecular fragments) with biological activity (e.g., active or inactive, toxic or not toxic).  The program can work with small or large, chemically diverse, and mechanistically complex sets of data. The models can be used for either mechanistic studies of biological activity or for the prediction of biological effects for untested compounds.

I am also extending my computational modeling to bridge the gap between observed human and environmental health effects.  I am preparing a proposal to study which, if any, environmental chemicals are related to the incidence of breast cancer in a heavily industrialized area.  We are proposing to use medical Geographic Information System (GIS) maps of breast cancer mortality and morbidity overlaid with ”breast cancer potential” maps derived from predictive SAR breast cancer models (or in vivo data when available) and fate and transport models for agents released from Toxic Release Inventory sites.

In closing, I feel my work has importance for protecting human and environmental health as I seek to better understand the many roles that chemicals have in both causing disease and curing it.  As we successively identify which chemicals pose the most risk to us and the environment, our combined health can be improved as we rationally prioritize the expenditure of limited resources in order to control the most noxious of them (first).  Likewise, as we discover new drugs that are successively more target-specific, the public’s health will improve by not only lessening the burden of disease but also the burden of adverse secondary drug effects.

 

Selected Publications:

  1. Rosenkranz, H.S., S.L. Cunningham, R. Mermelstein, and A.R. Cunningham (2007) The challenge of testing chemicals for potential carcinogenicity using multiple short term assays. An analysis of a proposed test battery for hair dyes.  Mutation Research/Genetic Toxicology and Environmental Mutagenesis, http://dx.doi.org/10.1016/j.mrgentox.2007.05.008   
  2. Rosenkranz, H.R. and A.R. Cunningham (2005) Lack of predictivity of the rat lethality (LD50) test for ecological and human health effects.  Alternatives to Laboratory Animals, 33: 9-19.
  3. Cunningham, S.L., A.R. Cunningham, and B.W. Day (2005) CoMFA, HQSAR and molecular docking studies of butitaxel analogues with  b-tubulin.  Journal of Molecular Modeling, 11(1): 48-54.
  4. Cunningham, A.R., S.L. Cunningham, D.M. Consoer, S.T. Moss, and M.H. Karol (2005) Development of an information-intensive structure- activity relationship model and its application to human respiratory chemical sensitizers.  SAR and QSAR in Environmental Research, 16(3): 273-285.
  5. Cunningham, A.R., S.L. Cunningham, and H.R. Rosenkranz (2004) Structure activity approach to the identification of environmental estrogens: The MCASE approach.  SAR and QSAR in Environmental Research, 15(1): 55-67.
  6. Vanoirbeek, J.A.J., C. Mandervelt, A.R. Cunningham, P.H.M. Hoet, H.X. Hadewijch, H.M. Vanhooren, and B. Nemery (2003) Validity of methods to predict the respiratory sensitising potential of chemicals.  A study with a piperidinyl chlorotriazine derivative that caused an outbreak of occupational asthma.  Toxicological Sciences, 76: 338-346.
  7. Rosenkranz, H.R. and A.R. Cunningham (2003) A substructure-based SAR model for odor perception in humans relevant to health risk assessment.  SAR and QSAR in Environmental Research, 14(3): 215-222.
  8. Rosenkranz, H.S. and A.R. Cunningham (2003) Environmental persistence of chemicals and their carcinogenic risk to humans.  Mutation Research, 528: 81-91.

 

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