Preclinical modeling of multi-drug cancer therapies

Anticancer drugs typically are administered in the clinic in the form of mixtures, sometimes called combinations. Only in rare cases, however, are mixtures approved as drugs. Rather, research on mixtures tends to occur after single drugs have been approved. The goal of this research project was to develop modeling approaches that would encourage rational preclinical mixture design. To this end, a series of models were developed. First, several QSAR classification models were constructed to predict the cytotoxicity, oral clearance, and acute systemic toxicity of drugs. The QSAR models were applied to a set of over 115,000 natural compounds in order to identify promising ones for testing in mixtures. Second, an improved method was developed to assess synergistic, antagonistic, and additive effects between drugs in a mixture. This method, dubbed the MixLow method, is similar to the Median-Effect method, the de facto standard for assessing drug interactions. The primary difference between the two is that the MixLow method uses a nonlinear mixed-effects model to estimate parameters of concentration-effect curves, rather than an ordinary least squares procedure. Parameter estimators produced by the MixLow method were more precise than those produced by the Median-Effect Method, and coverage of Loewe index confidence intervals was superior. Third, a model was developed to predict drug interactions based on scores obtained from virtual docking experiments. This represents a novel approach for modeling drug mixtures and was more useful for the data modeled here than competing approaches. The model was applied to cytotoxicity data for 45 mixtures, each composed of up to 10 selected drugs. One drug, doxorubicin, was a standard chemotherapy agent and the others were well-known natural compounds including curcumin, EGCG, quercetin, and rhein. Predictions of synergism/antagonism were made for all possible fixed-ratio mixtures, cytotoxicities of the 10 best-scoring mixtures were tested, and drug interactions were assessed. Predicted and observed responses were highly correlated (r2 = 0.83). Results suggested that some mixtures allowed up to an 11-fold reduction of doxorubicin concentrations without sacrificing efficacy. Taken together, the models developed in this project present a general approach to rational design of mixtures during preclinical drug development. ^

Searching for the ideal DNA recognition code of covalent ligands

The combitiatorial approach restriction endonuclease protection selection and amplification REPSA was successfully used to determine ideal DNA interactions sites of covalent ligands. Unlike most other combinatorial methods, REPSA is based on inhibition of enzymatic cleavage by specific ligand-DNA complexes, which enables identification of binding sites of various ligands. However, the inherent nature of this technique posses a problem during selection of binding sites of covalent ligands. By modifying the technique according to the nature of the ligand, we demonstrate the flexibility of REPSA in identifying the preferred binding sites for monocovalent ligands, topoisomerase I and tallimustine, and the bicovalent ligand topoisomerase II. From among the preferred binding sites, we identified the consensus binding sequence of camptothecin induced topoisomerase I cleavage as ‘aGWT/Gc’, and tallimustine consensus sequences as ‘GTTCTA’ and ‘TTTTTTC’. We have shown for the first time that preferential binding of tallimustine occurs at sequences not previously reported. Furthermore, our data indicate that tallimustine is a novel DNA minor groove, guanine-specific alkylating agent. ^ Additionally, we have demonstrated in vivo that sequence-specific covalent DNA-binding small molecules have the ability to regulate transcription by inhibiting RNA polymerase II. Tallimustine, binding to its preferred sequences located in the 5′ untranslated region were an effective impediment for transcribing polymerase II. The ability of covalent binding small molecules to target predetermined DNA sequences located downstream of the promoter suggests a general approach for regulation of gene expression. ^

The illness narratives of Hispanic American women with coronary heart disease (CHD): An ethnographic approach

Purpose. To provide a descriptive representation of the illness narratives described by Hispanic American women with CHD. ^ Design. Focused ethnographic design. ^ Setting. One outpatient general medicine clinic, one nurse-managed health promotion clinic, and informants' homes in a large metropolitan city located in southeast Texas. ^ Sample. Purposeful sampling from two different sites resulted in 17 interviews being conducted with 14 informants. ^ Method. Focused ethnographic techniques were employed in the designation of participants for the study, data collection, analysis and re-presentation. Audiotaped interviews and fieldwork were transcribed verbatim and analyzed through an iterative process of data reduction, data display, drawing conclusions and verification. ^ Findings. The developing conceptual framework that emerged from the data is labeled after the overarching experience described by informants, the experience of Embodied Exhaustion. Embodied Exhaustion, as described in this study, refers to an ongoing, dynamic, indeterminate experience of mind-body exhaustion resulting from a complex constellation of biologic, psychological and social distresses occurring over the life course. The experience consists of three categories: Taking Care of Others, Wearing Down and Hurting Hearts. Two stabilizing forces were identified: Collective Self and Believing in God. ^ Conclusions. The findings of this study emphasize the importance of framing all research, theory and practice targeting Hispanic women with CHD within a sociocentric paradigm. Nursing is challenged to provide care that extends beyond the physical body of the patient to include the social context of illness, especially the family. ^

Distribution of quality adjusted life expectancy: A systems approach

Statistical methods are developed which assess survival data for two attributes; (1) prolongation of life, (2) quality of life. Health state transition probabilities correspond to prolongation of life and are modeled as a discrete-time semi-Markov process. Imbedded within the sojourn time of a particular health state are the quality of life transitions. They reflect events which differentiate perceptions of pain and suffering over a fixed time period. Quality of life transition probabilities are derived from the assumptions of a simple Markov process. These probabilities depend on the health state currently occupied and the next health state to which a transition is made. Utilizing the two forms of attributes the model has the capability to estimate the distribution of expected quality adjusted life years (in addition to the distribution of expected survival times). The expected quality of life can also be estimated within the health state sojourn time making more flexible the assessment of utility preferences. The methods are demonstrated on a subset of follow-up data from the Beta Blocker Heart Attack Trial (BHAT). This model contains the structure necessary to make inferences when assessing a general survival problem with a two dimensional outcome. ^

MEASURING THE EFFECT OF ILLNESS ON THE EXPECTED REMAINING SERVICE TIME FOR THE ACTIVE DUTY ARMY: A LIFE TABLE APPROACH

A life table methodology was developed which estimates the expected remaining Army service time and the expected remaining Army sick time by years of service for the United States Army population. A measure of illness impact was defined as the ratio of expected remaining Army sick time to the expected remaining Army service time. The variances of the resulting estimators were developed on the basis of current data. The theory of partial and complete competing risks was considered for each type of decrement (death, administrative separation, and medical separation) and for the causes of sick time.^ The methodology was applied to world-wide U.S. Army data for calendar year 1978. A total of 669,493 enlisted personnel and 97,704 officers were reported on active duty as of 30 September 1978. During calendar year 1978, the Army Medical Department reported 114,647 inpatient discharges and 1,767,146 sick days. Although the methodology is completely general with respect to the definition of sick time, only sick time associated with an inpatient episode was considered in this study.^ Since the temporal measure was years of Army service, an age-adjusting process was applied to the life tables for comparative purposes. Analyses were conducted by rank (enlisted and officer), race and sex, and were based on the ratio of expected remaining Army sick time to expected remaining Army service time. Seventeen major diagnostic groups, classified by the Eighth Revision, International Classification of Diseases, Adapted for Use In The United States, were ranked according to their cumulative (across years of service) contribution to expected remaining sick time.^ The study results indicated that enlisted personnel tend to have more expected hospital-associated sick time relative to their expected Army service time than officers. Non-white officers generally have more expected sick time relative to their expected Army service time than white officers. This racial differential was not supported within the enlisted population. Females tend to have more expected sick time relative to their expected Army service time than males. This tendency remained after diagnostic groups 580-629 (Genitourinary System) and 630-678 (Pregnancy and Childbirth) were removed. Problems associated with the circulatory system, digestive system and musculoskeletal system were among the three leading causes of cumulative sick time across years of service. ^