Sildenafil and analogous phosphodiesterase type 5 (PDE-5) inhibitors in herbal food supplements sampled on the Dutch market.

Herbal food supplements, claiming to enhance sexual potency, may contain deliberately added active pharmacological ingredients (APIs) that can be used for the treatment of erectile dysfunction (ED). The aim of this study was to determine whether herbal food supplements on the Dutch market indeed contain APIs that inhibit phosphodiesterase type 5 (PDE-5) inhibitors, such as sildenafil and analogous PDE-5 inhibitors. Herbal food supplements intended to enhance sexual potency (n = 71), and two soft drinks, were sampled from 2003 up to and including 2012. In 23 herbal supplements, nine different PDE-5 inhibitors were identified; in a few cases (n = 3), more than one inhibitor was indentified. The presence of these APIs was however not stated on the label. The concentrations of PDE-5 inhibitors per dose unit were analysed. Furthermore, the potential pharmacologically active properties of the detected PDE-5 inhibitors were estimated by using data from the scientific and patent literature regarding (1) in vitro PDE-5 activity, (2) reported effective doses of registered drugs with PDE-5 inhibitor activity and (3) similarity to other structural analogues. It was concluded that 18 of the 23 herbal food supplements, when used as recommended, would have significant pharmacological effects due to added APIs. Adequate use of existing regulation and control measures seems necessary to protect consumers against the adverse effects of these products.

Consistency of FMEA used in the validation of analytical procedures.

In order to explore the consistency of the outcome of a Failure Mode and Effects Analysis (FMEA) in the validation of analytical procedures, an FMEA was carried out by two different teams. The two teams applied two separate FMEAs to a High Performance Liquid Chromatography-Diode Array Detection-Mass Spectrometry (HPLC-DAD-MS) analytical procedure used in the quality control of medicines. Each team was free to define their own ranking scales for the probability of severity (S), occurrence (O), and detection (D) of failure modes. We calculated Risk Priority Numbers (RPNs) and we identified the failure modes above the 90th percentile of RPN values as failure modes needing urgent corrective action; failure modes falling between the 75th and 90th percentile of RPN values were identified as failure modes needing necessary corrective action, respectively. Team 1 and Team 2 identified five and six failure modes needing urgent corrective action respectively, with two being commonly identified. Of the failure modes needing necessary corrective actions, about a third were commonly identified by both teams. These results show inconsistency in the outcome of the FMEA. To improve consistency, we recommend that FMEA is always carried out under the supervision of an experienced FMEA-facilitator and that the FMEA team has at least two members with competence in the analytical method to be validated. However, the FMEAs of both teams contained valuable information that was not identified by the other team, indicating that this inconsistency is not always a drawback.

Risk analysis by FMEA as an element of analytical validation.

We subjected a Near-Infrared (NIR) analytical procedure used for screening drugs on authenticity to a Failure Mode and Effects Analysis (FMEA), including technical risks as well as risks related to human failure. An FMEA team broke down the NIR analytical method into process steps and identified possible failure modes for each step. Each failure mode was ranked on estimated frequency of occurrence (O), probability that the failure would remain undetected later in the process (D) and severity (S), each on a scale of 1-10. Human errors turned out to be the most common cause of failure modes. Failure risks were calculated by Risk Priority Numbers (RPNs)=O x D x S. Failure modes with the highest RPN scores were subjected to corrective actions and the FMEA was repeated, showing reductions in RPN scores and resulting in improvement indices up to 5.0. We recommend risk analysis as an addition to the usual analytical validation, as the FMEA enabled us to detect previously unidentified risks.