Nitin Rathore

A coarse grain model for DNA

Understanding the behavior of DNA at the molecular level is of considerable fundamental and engineering importance. While adequate representations of DNA exist at the atomic and continuum level, there is a relative lack of models capable of describing the behavior of DNA at mesoscopic length scales. We present a mesoscale model of DNA that reduces the complexity of a nucleotide to three interactions sites, one each for the phosphate, sugar, and base, thereby rendering the investigation of DNA up to a few microns in length computationally tractable. The charges on these sites are considered explicitly. The model is parametrized using thermal denaturation experimental data at a fixed salt concentration. The validity of the model is established by its ability to predict several aspects of DNA behavior, including salt-dependent melting, bubble formation and rehybridization, and the mechanical properties of the molecule as a function of salt concentration.

Optimal allocation of replicas in parallel tempering simulations

We have studied the efficiency of parallel tempering simulations for a variety of systems including a coarse-grained protein, an atomistic model polypeptide, and the Lennard-Jones fluid. A scheme is proposed for the optimal allocation of temperatures in these simulations. The method is compared to the existing empirical approaches used for this purpose. Accuracy associated with the computed thermodynamic quantities such as specific heat is also computed and their dependence on the trial-exchange acceptance rate is reported.

Current perspectives on stability of protein drug products during formulation, fill and finish operations.

Commercialization of protein‐based therapeutics is a challenging task in part due to the difficulties in maintaining protein solutions safe and efficacious throughout the drug product development process, storage, transportation and patient administration. Bulk drug substance goes through a series of formulation, fill and finish operations to provide the final dosage form in the desired formulation and container or delivery device. Different process parameters during each of these operations can affect the purity, activity and efficacy of the final product. Common protein degradation pathways and the various physical and chemical factors that can induce such reactions have been extensively studied for years. This review presents an overview of the various formulation‐fill‐finish operations with a focus on processing steps and conditions that can impact product quality. Various manufacturing operations including bulk freeze‐thaw, formulation, filtration, filling, lyophilization, inspection, labeling, packaging, storage, transport and delivery have been reviewed. The article highlights our present day understanding of protein instability issues during biopharmaceutical manufacturing and provides guidance on process considerations that can help alleviate these concerns.

Density of states simulations of proteins

A modified version of a recently introduced algorithm that calculates density of states by performing a random walk in energy space has been proposed and implemented to study protein folding in a continuum. A united atom representation and the CHARMM19 [B. R. Brooks, R. E. Bruccoleri, B. D. Olafson, D. J. States, S. Swaminathan, and M. Karplus, J. Comput. Chem. 4, 187 (1983)] force field are employed for these simulations. This method permits estimation of the density of states of a protein via a random walk in the energy space, thereby allowing the system to escape from local free-energy minima with relative ease. Unlike the earlier formulation that showed slow convergence for continuum simulations, this methodology is designed to achieve better sampling and faster convergence. The modified method is used to examine folding transitions of two peptides: deca-alanine and Met-enkephalin. Protein folding both with and without an implicit solvent (solvent accessible surface area model) has been studied to validate the usefulness of the proposed algorithm.

Monte Carlo simulation of proteins through a random walk in energy space

A Monte Carlo algorithm that performs a random walk in energy space has been used to study random coil–helix and random coil–beta sheet transitions in model proteins. This method permits estimation of the density of states of a protein via a random walk on the energy surface, thereby allowing the system to escape from local free-energy minima with relative ease. A cubic lattice model and a knowledge based force field are employed for these simulations. It is shown that, for a given amino acid sequence, the method is able to fold long polypeptides reproducibly. Its results compare favorably with those of annealing and parallel tempering simulations, which have been used before in the same context. This method is used to examine the effect of amino acid sequence and chain length on the folding of several designer polypeptides.

An Entropic Perspective of Protein Stability on Surfaces

The interaction of proteins with surfaces regulates numerous processes in nature, science, and technology. In many applications, it is desirable to place proteins on surfaces in an active state, and tethering represents one manner in which to accomplish this. However, a clear understanding of how tether placement and design affects protein activity is lacking. Available theoretical models predict that proteins will be stabilized when tethered to substrates. Such models suggest that the surface reduces the number of states accessible to the unfolded state of the protein, thereby reducing the entropic cost of folding on the surface compared to the bulk case. Recent studies, however, have shown that this stabilization is not always seen. The purpose of this article is to determine the validity of the theory with a thorough thermodynamic analysis of the folding of peptides attached to surfaces. Configuration-temperature-density-of-states Monte Carlo simulations are used to examine the behavior of four different peptides of different secondary and tertiary structure. It is found that the surface does reduce the entropic cost of folding for tethered peptides, as the theory suggests. This effect, however, does not always translate into improved stability because the surface may also have a destabilizing enthalpic effect. The theory neglects this effect and assumes that the enthalpy of folding is the same on and off the surface. Both the enthalpic and entropic contributions to the stability are found to be topology- and tether-placement-specific; we show that stability cannot be predicted a priori. A detailed analysis of the folding of protein A shows how the same protein can be both stabilized and destabilized on a surface depending upon how the tethering enhances or hinders the ability of the peptide to form correct tertiary structures.

Molecular simulation of the reversible mechanical unfolding of proteins.

In this work we have combined a Wang-Landau sampling scheme [F. Wang and D. Landau, Phys. Rev. Lett. 86, 2050 (2001)] with an expanded ensemble formalism to yield a simple and powerful method for computing potentials of mean force. The new method is implemented to investigate the mechanical deformation of proteins. Comparisons are made with analytical results for simple model systems such as harmonic springs and Rouse chains. The method is then illustrated on a model 15-residue alanine molecule in an implicit solvent. Results for mechanical unfolding of this oligopeptide are compared to those of steered molecular dynamics calculations.

Configurational temperature density of states simulations of proteins.

A novel method has been implemented to compute the density of states of proteins. A united atom representation and the CHARMM (Brooks et al., 1983) force-field parameters have been adopted for all the simulations. In this approach, an intrinsic temperature is computed based on configurational information about the protein. A random walk is performed in potential energy space and the configurational temperature is collected as a function of potential energy of the system. The density of states is then calculated by integrating the reciprocal of temperature. Unlike previously available methods, this approach does not involve calculations based on histograms of stochastic visits to distinct energy states. It is found that the proposed method is more efficient than earlier, related schemes for simulation of protein folding. Furthermore, it directly provides thermodynamic information, including free energies. The usefulness of the method is discussed by presenting results of simulations of the 16-residue beta-hairpin taken from the C-terminal fragment (41-56) of protein G.

Characterization of protein rheology and delivery forces for combination products

Characterization of a protein-device combination product over a wide range of operating parameters defined by end-user requirements is critical for developing a product presentation that is convenient for patient use. In addition to the device components, several product attributes, such as product rheology and product-container interactions, govern the functionality of a delivery system. This article presents results from a characterization study conducted for a high-concentration antibody product in a prefilled syringe. Analytical models are used to study the rheological behavior and to estimate delivery forces over a broad design space comprising temperature, concentration, and shear stress. Data suggest that high-viscosity products may exhibit significant shear thinning under the shear rates encountered under desired injection times.

Variability in syringe components and its impact on functionality of delivery systems.

Prefilled syringes and autoinjectors are becoming increasingly common for parenteral drug administration primarily due to the convenience they offer to the patients. Successful commercialization of such delivery systems requires thorough characterization of individual components. Complete understanding of various sources of variability and their ranking is essential for robust device design. In this work, we studied the impact of variability in various primary container and device components on the delivery forces associated with syringe injection. More specifically, the effects of barrel size, needle size, autoinjector spring force, and frictional forces have been evaluated. An analytical model based on underlying physics is developed that can be used to fully characterize the design space for a product delivery system. LAY ABSTRACT: Use of prefilled syringes (syringes prefilled with active drug) is becoming increasingly common for injectable drugs. Compared to vials, prefilled syringes offer higher dose accuracy and ease of use due to fewer steps required for dosage. Convenience to end users can be further enhanced through the use of prefilled syringes in combination with delivery devices such as autoinjectors. These devices allow patients to self-administer the drug by following simple steps such as pressing a button. These autoinjectors are often spring-loaded and are designed to keep the needle tip shielded prior to injection. Because the needle is not visible to the user, such autoinjectors are perceived to be less invasive than syringes and help the patient overcome the hesitation associated with self-administration. In order to successfully develop and market such delivery devices, we need to perform an in-depth analysis of the components that come into play during the activation of the device and dose delivery. Typically, an autoinjector is activated by the press of a button that releases a compressed spring; the spring relaxes and provides the driving force to push the drug out of the syringe and into the site of administration. Complete understanding of the spring force, syringe barrel dimensions, needle size, and drug product properties is essential for robust device design. It is equally important to estimate the extent of variability that exists in these components and the resulting impact it could have on the performance of the device. In this work, we studied the impact of variability in syringe and device components on the delivery forces associated with syringe injection. More specifically, the effect of barrel size, needle size, autoinjector spring force, and frictional forces has been evaluated. An analytical model based on underlying physics is developed that can be used to predict the functionality of the autoinjector.