VI. Conclusão
O esforço conjunto de distintas áreas do saber, como a matemática e farmacologia, promove um avanço efetivo na investigação biomédica, atuando de forma complementar na criação e desenvolvimento de modelos explicativos e aplicáveis a situações práticas. A modelação matemática apresenta um crescente desenvolvimento e importância na sua aplicabilidade em áreas da farmacologia e medicina, na medida em que procura prever determinados problemas práticos antes de estes ocorrerem. É o caso do objeto de estudo da tese, onde se procurou modelar matematicamente a farmacocinética da carboplatina, em doentes com CPNPC avançado para individualização terapêutica.
A modelação partiu da definição de um modelo estrutural básico, que calcula as doses sem a presença de qualquer parâmetro de individualização, terminando na definição de um modelo de covariáveis, que possibilita a individualização de doses pelas características bio-antropométricas incluídas. De todo este processo, pode-se concluir que:
1. Modelo farmacocinético Estrutural básico de carboplatina é definido como bicompartimental, com eliminação de primeira ordem com estimação dos parâmetros farmacocinéticos clearance, volume de distribuição do compartimento central, constante de velocidade de distribuição do compartimento central para o periférico, constante de velocidade retorno do compartimento periférico para o central. Variabilidade interindividual exponencial para a clearance e volume de distribuição e aditiva fixa para as constantes de velocidade de distribuição e de retorno. Variabilidade residual exponencial e distinta por idade e aditiva fixa ao limite de deteção do método analítico.
2. A clearance foi modelada de forma distinta para os grupos etários. Nos adultos, relaciona com as covariáveis idade, de forma linear negativa, e peso, de forma linear positiva. A clearance para idosos apenas depende da creatinina sérica. O volume de distribuição do compartimento central aponta para a dependência com a idade e peso. As constantes de transferência intercompartimental não apresentam dependência com nenhuma covariável.
3. O modelo final foi validado por métodos internos e externos, verificando todas as condições. A validação interna, pelo método de bootstrap, indica que o modelo pode ser aplicado com sucesso a mais de 80% de populações com características semelhantes às dos indivíduos incluídos no estudo.
VI. Conclusão
4. A monitorização de carboplatina deve ser feita com recolha de amostras sanguíneas e determinação das concentrações plasmáticas de carboplatina livre, permitindo a estimação exata e precisa da clearance individual dos doentes a posteriori por métodos bayesianos. Esta metodologia pode ser aplicada em individualização posológica de carboplatina em doentes com CPNPC, sem necessidade de adequação à área debaixo da curva (AUC).
Por fim, o objetivo final da individualização terapêutica das doses de carboplatina em doentes com CPNPC em estado avançado foi conseguido, através das equações de cálculo da carboplatina que tomam em consideração as características individuais de cada doente, e são diferentes para adultos e idosos. Estas equações revelaram-se precisas e exatas com a informação das concentrações de carboplatina em pelo menos duas amostras sanguíneas. A aplicação destas fórmulas a casos práticos clínicos poderia colmatar muitas das falhas observadas ao nível da toxicidade inesperada, nomeadamente em doentes idosos.
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Anexos
Anexo A
Anexos
Anexo B
Anexos
Anexo C
Ficheiro controlo do modelo Final de covariáveis
$PROBLEM CP CARBOPLATINA - MODELO BICOMPARTIMENTAL.
$INPUT ID AMT RATE TIME CP=DV MDV IDADE PESO ALTURA AUC GRUPO CR IMC BS
$DATA CBPT2_2.csv IGNORE="#" $SUBROUTINES ADVAN3 TRANS1 $PK
IF (GRUPO.EQ.0) THEN
CL = (THETA(1) - THETA(6)*(IDADE-70) + (PESO-70)*THETA(8)) * EXP(ETA(1)) ELSE
CL = (THETA(1) - (CR/0.9)*THETA(7)) * EXP(ETA(5)) END IF
VC = (THETA(2) - (IDADE-70)*THETA(5) + (PESO-70) * THETA(9)) * EXP(ETA(2)) K12 = THETA(3) + ETA(3) K21 = THETA(4) + ETA(4) K = CL/VC S1=VC $ERROR IPRED = F Y = F * EXP(GRUPO*EPS(1)+(1-GRUPO)*EPS(3)) + EPS(2) $THETA (0.0000001, 5.19, 50) (0.00001, 8.74, 50) (0.00001, 0.6, 10) (0.00001, 0.6, 10) (0.6) (0.0000001, 0.3) (1) (0.07) (0.2)
Anexos $OMEGA (0.1) (0.1) (0.04 FIX) (0.04 FIX) (0.1) $SIGMA (0.007) (0.0025 FIX) (0.007)
$EST METHOD=1 INTERACTION SIGDIG=3 FILE=RESULTADOSVC5_A.EXT MAXEVAL=9000 NITER=500 PRINT=5 POSTHOC NOABORT
$COV SLOW PRINT=E
$TABLE ID TIME PRED RES WRES IPRED CP CPRED CWRES EPRED ERES NOAPPEND ONEHEADER FILE=vc5.fitPRINT
$TABLE ID CL VC K K12 K21 FIRSTONLY NOAPPEND NOPRINT FILE=ResultadosVc5_C.PAR