Critical Considerations in Anticancer Drug Development and Dosing Strategies: The Past, Present, and Future

Molecular View on the iRGD Peptide Binding Mechanism: Implications for Integrin Activity and Selectivity Profiles

Receptor-selective peptides are widely used as smart carriers for specific tumor-targeted delivery. A remarkable example is the cyclic nonapeptide iRGD (CRGDKPGDC, 1) that couples intrinsic cytotoxic effects with striking tumor-homing properties. These peculiar features are based on a rather complex multistep mechanism of action, where the primary event is the recognition of RGD integrins. Despite the high number of preclinical studies and the recent success of a phase I trial for the treatment of pancreatic ductal adenocarcinoma (PDAC), there is little information available about the iRGD three-dimensional (3D) structure and integrin binding properties. Here, we re-evaluate the peptide's affinity for cancer-related integrins including not only the previously known targets αvβ3 and αvβ5 but also the αvβ6 isoform, which is known to drive cell growth, migration, and invasion in many malignancies including PDAC. Furthermore, we use parallel tempering in the well-tempered ensemble (PT-WTE) metadynamics simulations to characterize the in-solution conformation of iRGD and extensive molecular dynamics calculations to fully investigate its binding mechanism to integrin partners. Finally, we provide clues for fine-tuning the peptide's potency and selectivity profile, which, in turn, may further improve its tumor-homing properties.

Exposure-response modeling improves selection of radiation and radiosensitizer combinations

A central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept. Combinations of radiation and radiosensitizers are evaluated based on their ability to induce tumor regression relative to toxicity and other potential costs. The approach is presented in the form of a case study where the objective is to find the most promising candidate out of three radiosensitizing agents. Data from a xenograft study is described using a nonlinear mixed-effects modeling approach and a previously-published tumor model for radiation and radiosensitizing agents. First, the most promising candidate is chosen under the assumption that all compounds are equally toxic. The impact of toxicity in compound selection is then illustrated by assuming that one compound is more toxic than the others, leading to a different choice of candidate.

Design and Analysis of a Sample-and-Hold CMOS Electrochemical Sensor for Aptamer-based Therapeutic Drug Monitoring

In this paper, we present the design and the analysis of an electrochemical circuit for measuring the concentrations of therapeutic drugs using structure-switching aptamers. Aptamers are single-stranded nucleic acids, whose sequence is selected to exhibit high affinity and specificity toward a molecular target, and change its conformation upon binding. This property, when coupled with a redox reporter and electrochemical detection, enables reagent-free biosensing with a sub-minute temporal resolution for in vivo therapeutic drug monitoring. Specifically, we design a chronoamperometry-based electrochemical circuit that measures the direct changes in the electron transfer (ET) kinetics of a methylene blue reporter conjugated at the distal-end of the aptamer. To overcome the high-frequency noise amplification issue when interfacing with a large-size (> 0.25 mm2) implantable electrode, we present a sample-and-hold (S/H) circuit technique in which the desired electrode potentials are held onto noiseless capacitors during the recording of the redox currents. This allows disconnecting the feedback amplifiers to avoid its noise injection while reducing the total power consumption. A prototype circuit implemented in 65-nm CMOS demonstrates a cell-capacitance-insensitive input-referred noise (IRN) current of 15.2 pArms at a 2.5-kHz filtering bandwidth. We tested our system in human whole blood samples and measured the changes in the ET kinetics from the redox-labeled aptamers at different kanamycin concentrations. By employing principal component analysis (PCA) to compensate for the sampling errors, we report a molecular noise floor (at SNR = 1) of 3.1 µM with sub 1-sec acquisition time at 0.22-mW power consumption.

The Steps to Therapeutic Drug Monitoring: A Structured Approach Illustrated With Imatinib

Pharmacometric methods have hugely benefited from progress in analytical and computer sciences during the past decades, and play nowadays a central role in the clinical development of new medicinal drugs. It is time that these methods translate into patient care through therapeutic drug monitoring (TDM), due to become a mainstay of precision medicine no less than genomic approaches to control variability in drug response and improve the efficacy and safety of treatments. In this review, we make the case for structuring TDM development along five generic questions: 1) Is the concerned drug a candidate to TDM? 2) What is the normal range for the drug's concentration? 3) What is the therapeutic target for the drug's concentration? 4) How to adjust the dosage of the drug to drive concentrations close to target? 5) Does evidence support the usefulness of TDM for this drug? We exemplify this approach through an overview of our development of the TDM of imatinib, the very first targeted anticancer agent. We express our position that a similar story shall apply to other drugs in this class, as well as to a wide range of treatments critical for the control of various life-threatening conditions. Despite hurdles that still jeopardize progress in TDM, there is no doubt that upcoming technological advances will shape and foster many innovative therapeutic monitoring methods.

Learning Opportunities for Drug Repositioning via GWAS and PheWAS Findings

Drug repositioning for available medications can be preferred over traditional drug development, which requires substantially more effort to uncover new insights into medications and diseases. Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS) are two complimentary methods for finding novel associations between genes and diseases. We hypothesize that discoveries from these studies could be leveraged to find new targets for existing drugs. Thus, we propose a framework to learn opportunities for inferring such relationships via overlapped genes between disease-associated genes (e.g. GWAS and PheWAS findings) and drugtargeted genes. We use drug indications found in Medication Indication Resource (MEDI) as a gold standard to evaluate if drug indications learned from GWAS and PheWAS findings have clinical indications. We examined 151,011 <drug, GWAS phenotype> pairs from 987 drugs across 153 diseases and 763 pairs were statistically significant. Out of these 763 pairs, 16 of them were found to have clinical indications.