Precision Medicine Drug Research Articles

Generalized paired-agent kinetic model for in vivo quantification of cancer cell-surface receptors under receptor saturation conditions

New precision medicine drugs oftentimes act through binding to specific cell-surface cancer receptors, and thus their efficacy is highly dependent on the availability of those receptors and the receptor concentration per cell. Paired-agent molecular imaging can provide quantitative information on receptor status in vivo, especially in tumor tissue; however, to date, published approaches to paired-agent quantitative imaging require that only ‘trace’ levels of imaging agent exist compared to receptor concentration. This strict requirement may limit applicability, particularly in drug binding studies, which seek to report on a biological effect in response to saturating receptors with a drug moiety. To extend the regime over which paired-agent imaging may be used, this work presents a generalized simplified reference tissue model (GSRTM) for paired-agent imaging developed to approximate receptor concentration in both non-receptor-saturated and receptor-saturated conditions. Extensive simulation studies show that tumor receptor concentration estimates recovered using the GSRTM are more accurate in receptor-saturation conditions than the standard simple reference tissue model (SRTM) (% error (mean ± sd): GSRTM 0 ± 1 and SRTM 50 ± 1) and match the SRTM accuracy in non-saturated conditions (% error (mean ± sd): GSRTM 5 ± 5 and SRTM 0 ± 5). To further test the approach, GSRTM-estimated receptor concentration was compared to SRTM-estimated values extracted from tumor xenograft in vivo mouse model data. The GSRTM estimates were observed to deviate from the SRTM in tumors with low receptor saturation (which are likely in a saturated regime). Finally, a general ‘rule-of-thumb’ algorithm is presented to estimate the expected level of receptor saturation that would be achieved in a given tissue provided dose and pharmacokinetic information about the drug or imaging agent being used, and physiological information about the tissue. These studies suggest that the GSRTM is necessary when receptor saturation exceeds 20% and highlight the potential for GSRTM to accurately measure receptor concentrations under saturation conditions, such as might be required during high dose drug studies, or for imaging applications where high concentrations of imaging agent are required to optimize signal-to-noise conditions. This model can also be applied to PET and SPECT imaging studies that tend to suffer from noisier data, but require one less parameter to fit if images are converted to imaging agent concentration (quantitative PET/SPECT).

Differential selective pressure alters rate of drug resistance acquisition in heterogeneous tumor populations.

Recent drug discovery and development efforts have created a large arsenal of targeted and chemotherapeutic drugs for precision medicine. However, drug resistance remains a major challenge as minor pre-existing resistant subpopulations are often found to be enriched at relapse. Current drug design has been heavily focused on initial efficacy, and we do not fully understand the effects of drug selective pressure on long-term drug resistance potential. Using a minimal two-population model, taking into account subpopulation proportions and growth/kill rates, we modeled long-term drug treatment and performed parameter sweeps to analyze the effects of each parameter on therapeutic efficacy. We found that drugs with the same overall initial kill may exert differential selective pressures, affecting long-term therapeutic outcome. We validated our conclusions experimentally using a preclinical model of Burkitt’s lymphoma. Furthermore, we highlighted an intrinsic tradeoff between drug-imposed overall selective pressure and rate of adaptation. A principled approach in understanding the effects of distinct drug selective pressures on short-term and long-term tumor response enables better design of therapeutics that ultimately minimize relapse.

Data integration to prioritize drugs using genomics and curated data.

BackgroundGenomic alterations affecting drug target proteins occur in several tumor types and are prime candidates for patient-specific tailored treatments. Increasingly, patients likely to benefit from targeted cancer therapy are selected based on molecular alterations. The selection of a precision therapy benefiting most patients is challenging but can be enhanced with integration of multiple types of molecular data. Data integration approaches for drug prioritization have successfully integrated diverse molecular data but do not take full advantage of existing data and literature.ResultsWe have built a knowledge-base which connects data from public databases with molecular results from over 2200 tumors, signaling pathways and drug-target databases. Moreover, we have developed a data mining algorithm to effectively utilize this heterogeneous knowledge-base. Our algorithm is designed to facilitate retargeting of existing drugs by stratifying samples and prioritizing drug targets. We analyzed 797 primary tumors from The Cancer Genome Atlas breast and ovarian cancer cohorts using our framework. FGFR, CDK and HER2 inhibitors were prioritized in breast and ovarian data sets. Estrogen receptor positive breast tumors showed potential sensitivity to targeted inhibitors of FGFR due to activation of FGFR3.ConclusionsOur results suggest that computational sample stratification selects potentially sensitive samples for targeted therapies and can aid in precision medicine drug repositioning. Source code is available from http://csblcanges.fimm.fi/GOPredict/.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-016-0097-1) contains supplementary material, which is available to authorized users.

Thérapie ciblée des glioblastomes : bilan et perspectives

Glioblastoma is the most common primary malignant brain tumor in adults. Despite very intensive treatments (i.e. maximal safe surgery, radiotherapy and cytotoxic chemotherapy), the prognosis of glioblastoma patients remains dismal with a median overall survival below 2 years. More effective and better tolerated treatments are needed. Over the last years, major advances have been accomplished in the molecular characterization of glioblastoma mainly thanks to high throughput molecular biology techniques. In parallel to these biological advances, pharmacological progresses have been made with the development of molecular targeted therapies or precision medicine or smart drugs targeting these molecular alterations detected in cancer cells but absent in normal cells of the body. These targeted molecular therapies, more effective and less toxic, have already revolutionized the prognosis of several systemic cancers (e.g., melanoma, lung cancer). On the basis of our knowledge of glioblastoma molecular biology, these molecular targeted therapies also appear promising in glioblastomas. Anti-angiogenic drugs are the most advanced molecular targeted therapies in their evaluation. Promising results were observed in recurrent glioblastoma patients. Other molecular targeted therapies are currently under preclinical or clinical evaluations. Similarly, to several systemic cancers, molecular targeted therapies will most likely find quickly their place in the therapeutic arsenal directed against glioblastomas to improve survival and quality of ife of patients suffering from this cancer type.

Precision Medicine and Site-Specific Drug Delivery

Precision medicine is an approach to prevent and treat diseases that takes into account people’s individual variations in genes, environment, and lifestyle. The current Precision Medicine Initiative of the US Government is to “generate the scientific evidence needed to move the concept of precision medicine into clinical practice”. In the first approximation, precision medicine may provide a more accurate diagnosis of the disease such as cancer, but may not have the means to offer an improved therapy. The aim of drug targeting is to generate pharmacologically effective drug concentration at the site of disease while keeping a very low/minimal drug concentration in the rest of the body, away from the site of disease. Targeted drugs are thus “precision drugs” needed to bring precision medicine into the clinical practice. Focusing on the area of cancer therapy, this review examines the essential requirements that must be met for cell/tumour-targeted drug-delivery systems to work. It examines the progress to date and draws conclusions for an optimal paradigm for future drug-delivery systems development.