Mateen Raiyat

Bio: Mateen Raiyat is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Reaction rate. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Flow synthesis kinetics for lomustine, an anti-cancer active pharmaceutical ingredient

Abstract: Continuous flow synthesis of active pharmaceutical ingredients (APIs) can offer access to process conditions that are otherwise hazardous when operated in batch mode, resulting in improved mixing and heat transfer, which enables higher yields and greater reaction selectivity. Reaction kinetic parameter estimation from flow synthesis data is an essential activity for the development of process models for drug substance manufacturing unit operations and systems, facilitating a reduction of experimental effort and accelerating development. The flow synthesis of lomustine, an anti-cancer API, in two flow reactors (carbamylation + nitrosation stages) was recently demonstrated by Jaman et al. (Org. Process Res. Dev., 2019, 23, 334). In this study, we postulate kinetic rate laws based on hereby proposed reaction mechanisms presented for the first time in the literature for this API synthesis. We then perform kinetic parameter regression for the proposed rate laws, on the basis of published data, towards establishing reactor models. For the carbamylation (irreversible reaction), we compare two candidate reaction rate laws, an overall third-order rate law (first-order in each reagent) deriving best fit. For the nitrosation, we propose two substitution reactions on the basis of published mechanisms (a rate-limiting equilibrium step, followed by a fast irreversible reaction) with very good model fit.

Continuous Flow Synthesis of Anticancer Drugs.

Abstract: Continuous flow chemistry is by now an established and valued synthesis technology regularly exploited in academic and industrial laboratories to bring about the improved preparation of a variety of molecular structures. Benefits such as better heat and mass transfer, improved process control and safety, a small equipment footprint, as well as the ability to integrate in-line analysis and purification tools into telescoped sequences are often cited when comparing flow to analogous batch processes. In this short review, the latest developments regarding the exploitation of continuous flow protocols towards the synthesis of anticancer drugs are evaluated. Our efforts focus predominately on the period of 2016–2021 and highlight key case studies where either the final active pharmaceutical ingredient (API) or its building blocks were produced continuously. It is hoped that this manuscript will serve as a useful synopsis showcasing the impact of continuous flow chemistry towards the generation of important anticancer drugs.

Mechanistic insights into amination via nucleophilic aromatic substitution

Abstract: Extensive kinetic investigations were performed to identify novel reaction pathways and assess their feasibility to resolve contradictions in the reported understanding of an amination reaction via nucleophilic aromatic substitution.

Parameter estimation and estimability analysis in pharmaceutical models with uncertain inputs

Abstract: A methodology is proposed to aid parameter estimation in fundamental models of pharmaceutical processes. This methodology addresses situations with insufficient data to reliably estimate all parameters, when the estimation is complicated by uncertain independent variables. The proposed method uses an augmented sensitivity matrix to rank the combined set of parameters and uncertain inputs from most estimable to least estimable. An updated mean-squared-error criterion is then used to determine the appropriate parameters and inputs that should be estimated, based on the ranked list. A model for one step in a batch pharmaceutical production process with an uncertain initial reactant concentration is used to illustrate the method, revealing that the initial reactant concentration in each batch should be estimated along with three out of six model parameters. Nonestimable parameters are fixed at their initial values to prevent overfitting. The method will aid error-in-variables parameter estimation in many situations involving limited data.

Pharma PSE: a multiscale approach for reimagining pharmaceutical manufacturing

Abstract: Confronted with the global challenges including COVID-19, pharmaceutical manufacturing needs to simultaneously achieve long-term efficiency and short-term resilience. Process systems engineering (PSE) can provide scientific basis here, and in fact, PSE researchers have made significant contributions to pharma in the last decade. The author, after having worked for a global pharmaceutical company, initiated research on pharmaceutical process systems engineering: Pharma PSE. The research tackles different challenges in small molecules, biopharmaceuticals, and regenerative medicine, at the scales of molecules/cells, processes, and the society. This paper first introduces the viewpoint of Pharma PSE, followed by showcasing a research example that involved a range of computer-aided analyses at different scales. The multiscale approach of Pharma PSE can provide a new horizon to “reimagine” pharmaceutical manufacturing processes and beyond, towards establishment of a sustainable healthcare society.