Sökning: "optimizing drug development"

Visar resultat 1 - 5 av 21 avhandlingar innehållade orden optimizing drug development.

  1. 1. Pharmacometric tools to support translational drug development

    Författare :Rami Ayoun Alsoud; Ulrika S. H. Simonsson; Michael Lyons; Uppsala universitet; []
    Nyckelord :MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; drug development; pharmacometrics; pharmacokinetics; pharmacodynamics; non-linear mixed effect models; tuberculosis; dose selection; interspecies scaling; pediatric trials; Farmaceutisk vetenskap; Pharmaceutical Science;

    Sammanfattning : The use of model-informed drug development has been shown to save significant costs and improve decision making early in the drug development process. The work in this PhD thesis aimed to employ pharmacometric tools to support translational drug development from the preclinical to the late clinical stages. LÄS MER

  3. 2. Practical Optimal Experimental Design in Drug Development and Drug Treatment using Nonlinear Mixed Effects Models

    Författare :Joakim Nyberg; Andrew C. Hooker; Mats O. Karlsson; Sergei Leonov; Uppsala universitet; []
    Nyckelord :MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; Pharmacometrics; optimal design; nonlinear mixed effects models; robust design; optimizing drug development; population models; Pharmacokinetics and Drug Therapy; Farmakokinetik och läkemedelsterapi;

    Sammanfattning : The cost of releasing a new drug on the market has increased rapidly in the last decade. The reasons for this increase vary with the drug, but the need to make correct decisions earlier in the drug development process and to maximize the information gained throughout the process is evident. LÄS MER

  4. 3. Novel Pharmacometric Methods for Informed Tuberculosis Drug Development

    Författare :Oskar Clewe; Ulrika S.H. Simonsson; Mats O. Karlsson; Piet van der Graaf; Uppsala universitet; []
    Nyckelord :MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; pharmacokinetics; pharmacodynamics; PKPD; pharmacometric; nonlinear mixed-effects models; multistate tuberculosis pharmacometric model; general pharmacodynamic interaction model; general pulmonary distribution model; tuberculosis; rifampicin; isoniazid; ethambutol; Pharmaceutical Science; Farmaceutisk vetenskap;

    Sammanfattning : With approximately nine million new cases and the attributable cause of death of an estimated two millions people every year there is an urgent need for new and effective drugs and treatment regimens targeting tuberculosis. The tuberculosis drug development pathway is however not ideal, containing non-predictive model systems and unanswered questions that may increase the risk of failure during late-phase drug development. LÄS MER

  5. 4. Optimizing Chemotherapy in Childhood Acute Myeloid Leukemia

    Författare :Josefine Palle; Gudmar Lönnerholm; Britt-Marie Frost; Rolf Larsson; Henrik Schröder; Uppsala universitet; []
    Nyckelord :MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; childhood; acute myeloid leukemia; drug resistance; pharmacokinetics; chromosomal abnormalities; Down syndrome; MLL-rearrangement; t 9; 11 ; Paediatric medicine; Pediatrisk medicin;

    Sammanfattning : Despite major advances in our understanding of the biology of childhood acute myeloid leukemia (AML) and the development of new cytotoxic drugs, the prognosis of long-term survival is still only 60-65 %.In the present research, we studied the pharmacokinetics of drugs used in the induction therapy of childhood AML and performed in vitro drug sensitivity testing of leukemic cells from children with AML. LÄS MER

  7. 5. Improving Drug Discovery Decision Making using Machine Learning and Graph Theory in QSAR Modeling

    Författare :Ernst Ahlberg Helgee; Göteborgs universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; machine-learning; QSAR; descriptor importance; local and global models; method of manufactured solutions; automated compound optimization; drug design;

    Sammanfattning : During the last decade non-linear machine-learning methods have gained popularity among QSAR modelers. The machine-learning algorithms generate highly accurate models at a cost of increased model complexity where simple interpretations, valid in the entire model domain, are rare. LÄS MER