Subgroup-Based Adaptive (SUBA) Designs for Multi-arm Biomarker Trials

Targeted therapies based on biomarker profiling are becoming a mainstream direction of cancer research and treatment. Depending on the expression of specific prognostic biomarkers, targeted therapies assign different cancer drugs to subgroups of patients even if they are diagnosed with the same type of cancer by traditional means, such as tumor location. For example, Herceptin is only indicated for the subgroup of patients with HER2+ breast cancer, but not other types of breast cancer. However, subgroups like HER2+ breast cancer with effective targeted therapies are rare, and most cancer drugs are still being applied to large patient populations that include many patients who might not respond or benefit. Also, the response to targeted agents in humans is usually unpredictable. To address these issues, we propose subgroup-based adaptive (SUBA), designs that simultaneously search for prognostic subgroups and allocate patients adaptively to the best subgroup-specific treatments throughout the course of the trial. The main features of SUBA include the continuous reclassification of patient subgroups based on a random partition model and the adaptive allocation of patients to the best treatment arm based on posterior predictive probabilities. We compare the SUBA design with three alternative designs including equal randomization, outcome-adaptive randomization, and a design based on a probit regression. In simulation studies, we find that SUBA compares favorably against the alternatives.     

Strategies for Genomic and Proteomic Profiling of Cancers

Omics-based technology platforms have made new kinds of cancer profiling tests feasible. There are several valuable examples in clinical practice, and many more under development. A concerted, transparent process of discovery with lock-down of candidate assays and classifiers and clear specification of intended clinical use is essential. The Institute of Medicine has now proposed a three-stage scheme of confirming and validating analytical findings, validating performance on clinical specimens, and demonstrating explicit clinical utility for an approvable test (Micheel et al., Evolution of translational omics: lessons learned and path forward, 2012).     

Dynamic light scattering: a practical guide and applications in biomedical sciences

Dynamic light scattering (DLS), also known as photon correlation spectroscopy (PCS), is a very powerful tool for studying the diffusion behaviour of macromolecules in solution. The diffusion coefficient, and hence the hydrodynamic radii calculated from it, depends on the size and shape of macromolecules. In this review, we provide evidence of the usefulness of DLS to study the homogeneity of proteins, nucleic acids, and complexes of protein–protein or protein–nucleic acid preparations, as well as to study protein–small molecule interactions. Further, we provide examples of DLS’s application both as a complementary method to analytical ultracentrifugation studies and as a screening tool to validate solution scattering models using determined hydrodynamic radii.     

Enhanced sampling simulations to construct free-energy landscape of protein–partner substrate interaction

Molecular dynamics (MD) simulations using all-atom and explicit solvent models provide valuable information on the detailed behavior of protein–partner substrate binding at the atomic level. As the power of computational resources increase, MD simulations are being used more widely and easily. However, it is still difficult to investigate the thermodynamic properties of protein–partner substrate binding and protein folding with conventional MD simulations. Enhanced sampling methods have been developed to sample conformations that reflect equilibrium conditions in a more efficient manner than conventional MD simulations, thereby allowing the construction of accurate free-energy landscapes. In this review, we discuss these enhanced sampling methods using a series of case-by-case examples. In particular, we review enhanced sampling methods conforming to trivial trajectory parallelization, virtual-system coupled multicanonical MD, and adaptive lambda square dynamics. These methods have been recently developed based on the existing method of multicanonical MD simulation. Their applications are reviewed with an emphasis on describing their practical implementation. In our concluding remarks we explore extensions of the enhanced sampling methods that may allow for even more efficient sampling.     

Structural aspects of nucleic acid-sensing Toll-like receptors

Invading pathogens elicit potent immune responses in cells through interactions between structurally conserved molecules derived from the pathogens and specialized innate immune receptors such as the Toll-like receptors (TLRs). Nucleic acid is one of the principal TLR ligands. Nucleic acid-sensing TLRs recognize an array of nucleic acids, including double-stranded RNA, single-stranded RNA, and DNAs with specific sequence motifs. Although ligand-induced dimerization is commonly observed followed by TLR activation, both the specific recognition mechanisms and the ligand–receptor interactions vary among different TLRs. In this review, we highlight our current understanding of how these receptors recognize their cognate ligands based on the recent advances in structural biology.     

Theoretical study of electron transfer process between fullerenes and neurotransmitters; acetylcholine,dopamine, serotonin and epinephrine in nanostructures [neurotransmitters].C<Subscript><Emphasis Type="Italic">n</Emphasis></Subscript> complexes

Neurotransmitters are the compounds which allow the transmission of signals from one neuron to the next across synapses. They are the brain chemicals that communicate information throughout brain and body. Fullerenes are a family of carbonallotropes, molecules composed entirely of carbon, that take the forms of spheres, ellipsoids, and cylinders. Various empty carbon fullerenes (C_n) with different carbon atoms have been obtained and investigated. Topological indices have been successfully used to construct effective and useful mathematical methods to establish clear relationships between structural data and the physical properties of these materials. In this study, the number of carbon atoms in the fullerenes was used as an index to establish a relationship between the structures of neurotransmitters (NTs) acetylcholine (AC) 1, dopamine (DP) 2, serotonin (SE) 3, and epinephrine (EP) 4 as the well-known redox systems and fullerenes C_n (n = 60, 70, 76, 82, and 86) which create [NT].C_n; A-1 to A-5 up to D-1 to D-5. The relationship between the number of carbon atoms and the free energy of electron transfer (ΔG_et(n); n = 1–4) is assessed using the Rehm-Weller equation for A-1 to A-5 up to D-1 to D-5 supramolecular [NT].C_n complexes. The calculations are presented for the four reduction potentials (^Red.E₁ to ^Red.E₄) of fullerenes C_n. The results were used to calculate the four free energy values of electron transfer (ΔG_et(1) to ΔG_et(4)) of the supramolecular complexes A-1 to A-8 up to D-1 to D-8 for fullerenes C₆₀ to C₁₂₀. The first to fourth free activation energy values of electron transfer and the maximum wavelength of the electron transfers, ΔG^#_et(n) and λ_et (n = 1–4), respectively, were also calculated in this study for A-1 to A-8 up to D-1 to D-8 in accordance with the Marcus theory.