A DFT study of the degradation mechanism of anticancer drug carmustine in an aqueous medium

First-principles study of a SiC nanosheet as an effective material for nitrosourea and carmustine anti-cancer drug delivery

The development of novel nanosheet-based drug delivery systems requires a systematic understanding of the interactions between the drug and the nanosheet carrier under various physiological environments. In this work, we investigated electronic and quantum molecular descriptors of a SiC monolayer adsorbed with the anticancer drugs nitrosourea (NU) and carmustine (BCNU) using density functional theory (DFT). Our calculations revealed negative adsorption energies for both drugs, indicating a spontaneous and energetically favorable adsorption process. Density of states and orbital population analysis studies revealed that both drugs are capable of significantly (>30%) narrowing the gap between HOMO and LUMO, depending on the configuration of the adsorption complex. Furthermore, the electronic and quantum molecular descriptors were investigated in gas and water mediums to explore the effect of the solvent on the adsorption process. Our calculations predict a higher narrowing of the HOMO-LUMO gap in the water phase compared to the gas phase. Besides, a modest reduction in global hardness and a marked increase in the global electrophilicity index were observed after the adsorption of the drug molecules by the SiC nanosheet, indicating its high reactivity towards both NU and BCNU. Changing the medium to water showed a maximum 2× increase in the global electrophilicity index of the nanosheet for NU and a maximum 7× increase for BCNU. Additionally, the thermodynamic study of the adsorption process indicates that the formation energies at high temperatures are smaller than those at low temperatures, unfolding the potential of SiC nanosheet for application in the phototherapy of these drugs.

Molecular Mechanisms Involved in the Chemical Instability of ONC201 and Methods to Counter Its Degradation in Solution

Glioblastoma is one of the most common and aggressive forms of brain tumor, a rare disease for which there is a great need for innovative therapies. ONC201, a new drug substance, has been used in a compassionate treatment program where the choice of dosage form and regimen have yet to be justified. The prior knowledge needed to anticipate ONC201 stability problems has recently been partially addressed, by (i) showing that ONC201 is sensitive to light and oxidation and (ii) identifying the molecular structures of the main degradation products formed. The aim of the work presented here was to improve our understanding of the degradation pathways of ONC201 using data from ab initio calculations and experimental work to supplement the structural information we already published. The C-H bonds located αto the amine of the tetrahydropyridine group and those located alpha to the imine function of the dihydroimidazole group exhibit the lowest bond dissociation energies (BDEs) within the ONC201 molecule. Moreover, these values drop well below 90 kcal.mol-1 when ONC201 is in an excited state (S1; T1). The structures of the photoproducts we had previously identified are consistent with these data, showing that they would have resulted from radical processes following the abstraction of alpha hydrogens. Concerning ONC201's sensitivity to oxidation, the structures of the oxidation products matched the critical points revealed through mapped electrostatic potential (MEP) and average local ionization energy (ALIE). The data obtained from ab initio calculations and experimental work showed that the reactivity of ONC201 to light and oxidation conditions is highly dependent on pH. While an acidic environment (pH < 6) contributes to making ONC201 quantitatively more stable in solution in the face of oxidation and photo-oxidation, it nevertheless seems that certain chemical groups in the molecule are more exposed to nucleophilic attacks, which explains the variation observed in the profile of degradation products formed in the presence of certain antioxidants tested. This information is crucial to better understand the stability results in the presence of antioxidant agents and to determine the right conditions for them to act.

N-oxidation Regioselectivity and Risk Prediction Using DFT-ALIE Calculations

INTRODUCTION

The formation of N-oxide degradants is a major concern in development of new drugs due to potential effects on a compound's pharmacological activity. Such effects include but are not limited to solubility, stability, toxicity, and efficacy. In addition, these chemical transformations can impact physicochemical properties that affect drug manufacturability. Hence identification and control of N-oxide transformations is of critical importance in the development of new therapeutics.

OBJECTIVE

This study describes the development of an in-silico approach to identify N-oxide formation in APIs with respect to autoxidation.

METHODS

Average Local Ionization Energy (ALIE) calculations were carried out using molecular modeling techniques and application of Density Functional Theory (DFT) at the B3LYP/6-31G(d,p) level of theory. A total of 257 nitrogen atoms and 15 different oxidizable nitrogen types were used in developing this method.

RESULTS

The results show that ALIE could be reliably used to predict the most susceptible nitrogen for N-oxide formation. A risk scale was developed that rapidly categorizes nitrogen's oxidative vulnerabilities as small, medium, or high.

CONCLUSIONS

The developed process presents a powerful tool to identify structural susceptibilities for N-oxidation as well as enabling rapid structure elucidation in resolving potential experimental ambiguities.

Emerging Landscape of Computational Modeling in Pharmaceutical Development

Computational chemistry applications have become an integral part of the drug discovery workflow over the past 35 years. However, computational modeling in support of drug development has remained a relatively uncharted territory for a significant part of both academic and industrial communities. This review considers the computational modeling workflows for three key components of drug preclinical and clinical development, namely, process chemistry, analytical research and development, as well as drug product and formulation development. An overview of the computational support for each step of the respective workflows is presented. Additionally, in context of solid form design, special consideration is given to modern physics-based virtual screening methods. This covers rational approaches to polymorph, coformer, counterion, and solvent virtual screening in support of solid form selection and design.