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Implementation of SPC Methodologies by Developing a QbD in the PQR’s of Oral Liquid Drug Formulations
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Orientador(es)
Resumo(s)
A integração de metodologias de Controlo Estatístico do Processo (SPC) no
desenvolvimento de Quality by Design (QbD) representa um avanço crucial na indústria
farmacêutica, assegurando a qualidade dos produtos através da monitorização e controlo
sistemáticos. Este estudo aplica princípios de Quality by Design retrospetivo (rQbD) aos
Relatórios de Revisão da Qualidade do Produto (PQR) de formulações líquidas não estéreis,
com o objetivo de melhorar a compreensão do processo, identificar fontes de variabilidade e
implementar estratégias de melhoria baseadas em dados.
Para alcançar estes objetivos, adotou-se uma abordagem estruturada, integrando a Análise
de Modos de Falha e Efeitos (FMEA) para classificar os Atributos Críticos de Qualidade
(CQAs), Parâmetros Críticos do Processo (CPPs) e Atributos Críticos Materiais (CMAs). Foi
utilizado diagrama de Ishikawa para identificar potenciais fontes de variabilidade, enquanto
ferramentas de SPC, como as Cartas de Controlo (CCs) de Shewhart para valores individuais e
amplitude móvel, permitiram a deteção de desvios estatísticos no processo. Além disso, foram
aplicados Modelos Autorregressivos Integrados de Médias Móveis (ARIMA) para avaliar dados
autocorrelacionados, permitindo a previsão de tendências e um melhor controlo do processo.
Os resultados indicam que CQAs como pH, teor de API e distribuição de pesos
moleculares apresentam variabilidade que requer estratégias de monitorização melhoradas. A
análise do volume dispensado como CPP, através da modelação segundo o modelo ARIMA e
CCs, destacou o impacto da calibração dos equipamentos e das intervenções dos operadores na
estabilidade do processo. Estudos de correlação entre as propriedades das matérias-primas e a
qualidade do produto final sugerem oportunidades para a utilização de modelos preditivos na
otimização da produção.
Ao integrar metodologias de rQbD e SPC nos PQRs, este estudo estabelece um modelo
sistemático para a monitorização proativa do processo e a melhoria contínua da qualidade,
reforçando a conformidade regulatória e a eficiência operacional.
The integration of Statistical Process Control (SPC) methodologies into the Quality by Design (QbD) framework represents a crucial advancement in pharmaceutical manufacturing, ensuring product quality through systematic monitoring and control. This study applies retrospective QbD (rQbD) principles to Product Quality Review (PQR) reports of non-sterile liquid formulations to enhance process understanding, identify variability sources, and implement data-driven improvement strategies. To achieve this, a structured approach was adopted, incorporating Failure Mode and Effects Analysis (FMEA) to classify Critical Quality Attributes (CQAs), Critical Process Parameters (CPPs), and Critical Material Attributes (CMAs). Ishikawa diagrams were used to identify potential variability sources, while SPC tools such as Shewhart Control Charts (CCs) for individual and moving ranges enabling the detection of statistical process deviations. Additionally, Autoregressive Integrated Moving Average (ARIMA) models were applied to assess autocorrelated data, allowing for trend forecasting and refined process control. Results indicate that CQAs such as pH, API assay, and molecular weight distribution exhibit variability requiring enhanced monitoring strategies. Analysis of deliverable volume as a CPP, through ARIMA modeling and CCs, highlighted the impact of equipment calibration and operator adjustments on process stability. Correlation studies between raw material properties and final product quality suggest predictive modeling opportunities for optimizing manufacturing outcomes. By embedding rQbD and SPC methodologies into PQR reports, this study establishes a systematic framework for proactive process monitoring and continuous quality improvement, reinforcing regulatory compliance and operational efficiency.
The integration of Statistical Process Control (SPC) methodologies into the Quality by Design (QbD) framework represents a crucial advancement in pharmaceutical manufacturing, ensuring product quality through systematic monitoring and control. This study applies retrospective QbD (rQbD) principles to Product Quality Review (PQR) reports of non-sterile liquid formulations to enhance process understanding, identify variability sources, and implement data-driven improvement strategies. To achieve this, a structured approach was adopted, incorporating Failure Mode and Effects Analysis (FMEA) to classify Critical Quality Attributes (CQAs), Critical Process Parameters (CPPs), and Critical Material Attributes (CMAs). Ishikawa diagrams were used to identify potential variability sources, while SPC tools such as Shewhart Control Charts (CCs) for individual and moving ranges enabling the detection of statistical process deviations. Additionally, Autoregressive Integrated Moving Average (ARIMA) models were applied to assess autocorrelated data, allowing for trend forecasting and refined process control. Results indicate that CQAs such as pH, API assay, and molecular weight distribution exhibit variability requiring enhanced monitoring strategies. Analysis of deliverable volume as a CPP, through ARIMA modeling and CCs, highlighted the impact of equipment calibration and operator adjustments on process stability. Correlation studies between raw material properties and final product quality suggest predictive modeling opportunities for optimizing manufacturing outcomes. By embedding rQbD and SPC methodologies into PQR reports, this study establishes a systematic framework for proactive process monitoring and continuous quality improvement, reinforcing regulatory compliance and operational efficiency.
Descrição
Palavras-chave
Quality by Design (QbD) Product Quality Review (PQR) Statistical Process Control (SPC) ARIMA Models Control Charts Risk Management