Personalized medicine: changing the paradigm of drug development

A real-time in vitro assay as a potential predictor of in vivo tumor imaging properties

INTRODUCTION

Selective tumor targeting strategies based on cell surface molecules enable new personalized diagnosis and treatments, potentially lowering adverse effects and increasing efficacy. Radio-immunotargeting generally relies on a molecule binding to a cancer-specific target. It is therefore important to understand the properties of molecular interactions in their working environment and how to translate these properties measured in vitro into the in vivo molecular imaging situation.

METHODS

Time resolved interaction analysis in vitro was compared with a corresponding in vivo xenograft mouse model. The antibody fragment AbD15179 was labeled with (125)I or (111)In, and analyzed on cell lines with differing CD44v6 expression in vitro, and in a dual tumor xenograft model derived from the same cell lines. In vitro LigandTracer measurements were analyzed with TraceDrawer and Interaction Map. Conjugate sensitivity, kinetics, and signal-to-background ratios were assessed for both tumor cells in vitro and xenograft tumors in vivo.

RESULTS

In vitro results revealed a general biphasic appearance of a high- and a low-affinity interaction event. The (111)In-labeled fragment displayed the largest proportion of the high-affinity interaction with increased sensitivity and retention compared to (125)I-Fab. In vivo results were in agreement with in vitro data, with increased retention, higher sensitivity and better contrast for the (111)In-labeled fragment compared to (125)I.

CONCLUSIONS

Time resolved binding characteristics measured in vitro largely matched the in vivo performance for the conjugates, which is promising for future studies. In vitro time-resolved LigandTracer assays are efficient, rapid, and in this study shown to be able to predict in vivo outcomes.

ADVANCES IN KNOWLEDGE AND IMPLICATIONS FOR PATIENT CARE

Further studies are needed to confirm these findings, but the method is promising considering the ethical need to reduce the use of laboratory animals, as well as reducing costs for the development of tumor targeting compounds in the future.

Molecular Dynamics: New Frontier in Personalized Medicine

The field of drug discovery has witnessed infinite development over the last decade with the demand for discovery of novel efficient lead compounds. Although the development of novel compounds in this field has seen large failure, a breakthrough in this area might be the establishment of personalized medicine. The trend of personalized medicine has shown stupendous growth being a hot topic after the successful completion of Human Genome Project and 1000 genomes pilot project. Genomic variant such as SNPs play a vital role with respect to inter individual's disease susceptibility and drug response. Hence, identification of such genetic variants has to be performed before administration of a drug. This process requires high-end techniques to understand the complexity of the molecules which might bring an insight to understand the compounds at their molecular level. To sustenance this, field of bioinformatics plays a crucial role in revealing the molecular mechanism of the mutation and thereby designing a drug for an individual in fast and affordable manner. High-end computational methods, such as molecular dynamics (MD) simulation has proved to be a constitutive approach to detecting the minor changes associated with an SNP for better understanding of the structural and functional relationship. The parameters used in molecular dynamic simulation elucidate different properties of a macromolecule, such as protein stability and flexibility. MD along with docking analysis can reveal the synergetic effect of an SNP in protein-ligand interaction and provides a foundation for designing a particular drug molecule for an individual. This compelling application of computational power and the advent of other technologies have paved a promising way toward personalized medicine. In this in-depth review, we tried to highlight the different wings of MD toward personalized medicine.

Tamoxifen integromics and personalized medicine: dynamic modular transformations underpinning response to tamoxifen in breast cancer treatment

Recent advances in pharmacogenomics technologies allow bold steps to be taken towards personalized medicine, more accurate health planning, and personalized drug development. In this framework, systems pharmacology network-based approaches offer an appealing way for integrating multi-omics data and set the basis for defining systems-level drug response biomarkers. On the road to individualized tamoxifen treatment in estrogen receptor-positive breast cancer patients, we examine the dynamics of the attendant pharmacological response mechanisms. By means of an "integromics" network approach, we assessed the tamoxifen effect through the way the high-order organization of interactome (i.e., the modules) is perturbed. To accomplish that, first we integrated the time series transcriptome data with the human protein interaction data, and second, an efficient module-detecting algorithm was applied onto the composite graphs. Our findings show that tamoxifen induces severe modular transformations on specific areas of the interactome. Our modular biomarkers in response to tamoxifen attest to the immunomodulatory role of tamoxifen, and further reveal that it deregulates cell cycle and apoptosis pathways, while coordinating the proteasome and basal transcription factors. To the best of our knowledge, this is the first report that informs the fields of personalized medicine and clinical pharmacology about the actual dynamic interactome response to tamoxifen administration.