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IMPACT OF PLANTS ON ANTICANCER DRUGS METABOLISM: DEVELOPMENT OF A DATABASE TO FACILITATE THE PHARMACIST’S EXPERTISE (submitted in 2019)

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European Statement

Clinical Pharmacy Services

Author(s)

ANAIS AMAR, SIMON CLAUTRIER, MORGANE GIOVANELLI, REGINE CHEVRIER

Why was it done?

The phytotherapy market has continued to grow for several years. However, in oncology, concomitant use of plants with oral or injectable chemotherapies can be harmful. Plants can interact with many cytochromes (CYP), impacting on the biotransformation and kinetics of drugs. While grapefruit or St John’s wort are already recognised as interfering with many therapies, the impact of many plants remains unknown for healthcare professionals. Tools exist to evaluate their effects on drug metabolism, but the multiplication of sources delays and complicates the advice of pharmacists.

What was done?

Centralise information on plant metabolism on a single support by creating a database. Facilitate pharmacist’s expertise about interactions between plants and anticancer drugs.

How was it done?

To create the database, it was necessary to establish an exhaustive list of plants. Three sources of information have been used:
– Inventory of phytotherapy products marketed in 4 drugstores
– Census of plants consumed by patients seen in pharmaceutical consultation (PC)
– Consultation of websites specialized in phytotherapy
Then, an Excel table has been developed:
– each line corresponds to a plant
– each column corresponds respectively to 17 CYP, a transport protein (Pgp), estrogen-like (EL) and antioxidant (AO) properties of the plant.
A colour code has been defined according to the inhibitory (yellow), inductive (blue), EL (purple) and AO (red) action of the plant. If there is no interaction, the box remains blank.
Plant effects data were collected from Hedrine®, Oncolien®, MSKCC, RX list and Drugs.com websites.

What has been achieved?

Finally, 174 plants have been accounted in drugstores, 82 were identified during PC and 129 found on websites. If 10% of plants have an EL action and 16% an AO effect, approximately 30% have inductive and/or inhibitory action of at least one CYP and/or PgP. Since the tool’s creation: 91% of answers could be given immediately to patients compared to only 9% delayed (plants still unreferenced).

What next?

This database is an essential tool for answering questions from patients with anticancer drugs. It saves precious time and responsiveness during PC, but also during patient phone calls. However, critical work with divergent information between sources is to be expected. Currently, as a precaution, we don’t recommend the use of plants subject to such a contradiction.

SIMULATION CURVES MAY HELP TO ASSESS ANTIBIOTICS ORALISATION PROCEDURES (submitted in 2019)

European Statement

Clinical Pharmacy Services

Author(s)

Andreas von Ameln-Mayerhofer, Martin Breuling, Ina Geist

Why was it done?

In the context of antibiotic stewardship, rapid oralisation of a parenteral antibiotic is recommended in many antibiotic stewardship guidelines. Such a sequence therapy is easy to implement if both application pathways lead to comparable efficacy levels at the site of infection. However, this does not apply to all anti-infectives, in particular some beta-lactam antibiotics represent a challenge in therapy. Additionally, the information about this topic is very sparse in the literature.

What was done?

In order to achieve an improvement in antimicrobial prescriptions, we have addressed possible problems regarding oralisation of antibiotics. For this purpose, we graphically compared the simulated efficacy levels of parenteral and oral forms of beta-lactams.

How was it done?

We programmed a computer based procedure that allows a simulation of plasma levels of antibiotics upon intravenous versus oral administration. Based on the obtained data and EUCAST-based MIC-distributions for a set of bacteria, we assessed the respective putative clinical actions.

What has been achieved?

Our simulations show that some oral beta-lactams do not reach the PK/PD condition of a sufficient therapy (fT>MHK) in the approved dosage. The simulations have been used for education seminars with physicians and partly led to an improvement in oralisation procedures. Additionally, an oralisation standard has been established.

What next?

Our next step is to develop a special prescription form for oral antibiotics which will enable us to control prescription behaviour even more effectively. We plan to monitor the prescription habits for anti-infectives more closely before and after establishing the prescription form.

PERSONALIZED BUSULFAN THERAPY: IMPLEMENTATION OF A THERAPEUTIC DRUG MONITORING PROGRAM (TDM)

European Statement

Clinical Pharmacy Services

Author(s)

VERA DOMINGOS, VERA PIRES, SÍLVIA SANTOS, PATRÍCIA TRINDADE, ANA INÁCIO, ELZA CANDEIAS, SUSANA SIMÕES, PAULO PAIXÃO, NUNO MIRANDA, ANTÓNIO GOUVEIA

Why was it done?

Personalized BU dosing is considered because BU has a narrow therapeutic index and exposure have been associated with important clinical outcomes. High exposures have been associated with an increased risk of toxicities (acute graft-versus-host disease and veno-occlusive disease) and low exposures with graft rejection and relapse.
For this reason, in a multidisciplinary working group, we identified an opportunity to improve the treatment of your patients.

What was done?

Implementation of a TDM procedure for Busulfan (BU) in conditioning therapy for hematopoietic stem cell transplantation (HSCT). Study variability in PK parameters and evaluate TDM efficacy. From this initial period, we perform TDM on all patients under Bu conditioning.

How was it done?

The steps followed were:
1. Pre-implementation: bibliographic research, identify reference centers to perform BU plasma assay. Development and validation of a LC-MS / MS method by the National Institute of Forensic Medicine. PK analysis using ADAPT-5 software.
2. Pilot: Trial and assess the feasibility of the procedure. Cross-validation of the results with UMC Utrecht.
3. Implementation: clinical practice, doing the necessary dose adjustments. Prospective collection of clinical and PK data.

The main obstacle is the lack of analytical methodology in our center and the turnaround time.

What has been achieved?

We performed a preliminary analysis with 21 patients. Mean clearance (CL) was 0,19L/h*kg±0,05L/h*kg and volume of distribution 0,65L*kg±0,22L*kg. Body weight was the most predictive covariance.
CL was significantly different between patients 10 years old (p = 0,024) and over treatment (p=0,0191). The type of conditioning regimen didn’t show relation with the BU CL (p=0,0514).
TDM increased the number of patients with an optimal exposure (target AUC ±10%) from 42% to 83%.Dose was reduced in 10 patients (max 37%) and increased in 3 (max 19%). 1 patient maintained the prescribed dose.

What next?

TDM increased significantly the number of patients with optimal exposure to BU.
This procedure is pioneer at a national level and it relies on a strict protocol which includes collaboration with several hospital departments and other highly-specialized external centers. This can be used as a tool for other drugs and to empower the pharmacist as an active agent in the clinical setting.

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