Prescribers have a very broad spectrum of drugs to select from. More than 2,000 ingredients are in the U.S. drug market with thousands of diverse dosage forms and strengths and more than 200,000 NDCCODEs representing all their manufactured packages (1). When prescribing drugs to multimorbide patients the number of drugs prescribed can quickly increase to 5 and more which is known as polypharmacy.
What do mathematics related to such prescribing practices mean? The number of all potential polypharmacy medications may be computed using the binomial coefficient N!/n! * (N-n)!: When selecting 5 drugs out of 50 drugs, the number of different medications or combinations is 2,118,760; with 5 out of 500 drugs which cover the major active principles avoiding any redundancy error, for example by including two statins in the same prescription, this number grows to the incredibly high number of more than 255 billions and with 10 drugs this number amounts to far more than trillions. No wonder, that Favatella et al. (2) recently stated: “Not all possible combinations of therapies can be formally tested” and “Even with the extensive controlled trial evidence available on the use of a drug in patients who are receiving potentially interacting medications, data do not exist to inform on all decisions that clinicians and patients must make. Through extrapolation of the class effect, the available summarized data can be used to estimate the impact of unstudied DDIs based on their PK properties.” It has to be doubted, however, that the traditional pairwise drug drug interactions checks which are nowadays integrated in most of the EMR-, hospital-, and pharmacy software systems are appropriate to support this endeavour efficiently. This doubt is justified in particular as according to Watanabe et al. (3) the estimated annual cost of drug-related morbidity and mortality resulting from nonoptimized medication therapy was $528.4 billion, equivalent to 16% of total US health care expenditures in 2016 with probably not much change in the years thereafter. Patients taking five to nine medications have a 50% chance of an adverse drug interaction, increasing to 100% when they are taking 20 or more medications and Health Research Funding reports that polypharmacy accounts for almost 30% of all hospital admissions and is the fifth leading cause of death in the U.S (4).
Because of the immense complexity of the drug drug interaction problem especially in polypharmacy and the immense $ 528.4 billion annual costs evidently related to nonoptimized medications it may be concluded that the measures actually taken and the drug interaction checkers actually used by doctors, pharmacists and nurses to ensure patient’s drug safety are insufficient. Moreover, the proposal of Watanabe et al. for an expansion of comprehensive medication management programs by clinical pharmacists in collaborative practices with physicians and other prescribers to eliminate the avoidable costs of nonoptimized medications and to improve patient outcomes will fail if this expansion is not accompanied by a paradigm change with the introduction of new technology providing in the interest of more safety additional clinical software and simultaneously substantial time savings. It looks even comparably grossly negligent, if any car disposes for safety reasons of a dual circuit brake and a clinical software to avoid hazardous medical errors relies only on one traditional drug interaction checker not really ready for the requirements of personalized medicine and polypharmacy.
SCHOLZ DataBank in its more than 40-years-endeavour of drug database development focussed its efforts in the last ten years to answer the following key questions:
• Is there a way to show directly how one drug in a regimen is impacted by all others?
• How can multiple pharmacokinetic and pharmacodynamic interactions be best aggregated
and reconciled?
• How can these interactions be best reconciled with vital signs, major serious adverse effects,
and important labs?
• How can risk factors such as renal stages and pharmacogenomics be integrated?
• In which form can the complexity of the problem be translated for the user`‘s best
understanding?
• How can an optimizing process be organized in a systematic and user-friendly way?
The Adverse Drug Risk Control Panel (ADR CP) withs its e²CP, its MDDI and its High Performance Optimizer technology (5) provides now the solution to answer all the questions brought up, to overcome the inadequacies of traditional drug interaction checkers, and to initiate the paradigm change needed. It provides the all in one; overview of drug risks in patient medications. It reconciles the world of multiple drug drug interactions (MDDI) with the world of vital signs and serious adverse drug effects. It opens the door to fast and comprehensive optimization of multiple drug prescriptions. Thereby prescribers can even in complex scenarios of polypharmacy recognize at one glance where severe hazards of a medication may exist and harm the patient and how to find alternatives to avoid them. Please have a look at the following case study:
Please visit us for more information on scholzdatabank.com or write to [email protected] and make SCHOLZ DataBank the dual circuit brake of your clinical software!
Literature
What do mathematics related to such prescribing practices mean? The number of all potential polypharmacy medications may be computed using the binomial coefficient N!/n! * (N-n)!: When selecting 5 drugs out of 50 drugs, the number of different medications or combinations is 2,118,760; with 5 out of 500 drugs which cover the major active principles avoiding any redundancy error, for example by including two statins in the same prescription, this number grows to the incredibly high number of more than 255 billions and with 10 drugs this number amounts to far more than trillions. No wonder, that Favatella et al. (2) recently stated: “Not all possible combinations of therapies can be formally tested” and “Even with the extensive controlled trial evidence available on the use of a drug in patients who are receiving potentially interacting medications, data do not exist to inform on all decisions that clinicians and patients must make. Through extrapolation of the class effect, the available summarized data can be used to estimate the impact of unstudied DDIs based on their PK properties.” It has to be doubted, however, that the traditional pairwise drug drug interactions checks which are nowadays integrated in most of the EMR-, hospital-, and pharmacy software systems are appropriate to support this endeavour efficiently. This doubt is justified in particular as according to Watanabe et al. (3) the estimated annual cost of drug-related morbidity and mortality resulting from nonoptimized medication therapy was $528.4 billion, equivalent to 16% of total US health care expenditures in 2016 with probably not much change in the years thereafter. Patients taking five to nine medications have a 50% chance of an adverse drug interaction, increasing to 100% when they are taking 20 or more medications and Health Research Funding reports that polypharmacy accounts for almost 30% of all hospital admissions and is the fifth leading cause of death in the U.S (4).
Because of the immense complexity of the drug drug interaction problem especially in polypharmacy and the immense $ 528.4 billion annual costs evidently related to nonoptimized medications it may be concluded that the measures actually taken and the drug interaction checkers actually used by doctors, pharmacists and nurses to ensure patient’s drug safety are insufficient. Moreover, the proposal of Watanabe et al. for an expansion of comprehensive medication management programs by clinical pharmacists in collaborative practices with physicians and other prescribers to eliminate the avoidable costs of nonoptimized medications and to improve patient outcomes will fail if this expansion is not accompanied by a paradigm change with the introduction of new technology providing in the interest of more safety additional clinical software and simultaneously substantial time savings. It looks even comparably grossly negligent, if any car disposes for safety reasons of a dual circuit brake and a clinical software to avoid hazardous medical errors relies only on one traditional drug interaction checker not really ready for the requirements of personalized medicine and polypharmacy.
SCHOLZ DataBank in its more than 40-years-endeavour of drug database development focussed its efforts in the last ten years to answer the following key questions:
• Is there a way to show directly how one drug in a regimen is impacted by all others?
• How can multiple pharmacokinetic and pharmacodynamic interactions be best aggregated
and reconciled?
• How can these interactions be best reconciled with vital signs, major serious adverse effects,
and important labs?
• How can risk factors such as renal stages and pharmacogenomics be integrated?
• In which form can the complexity of the problem be translated for the user`‘s best
understanding?
• How can an optimizing process be organized in a systematic and user-friendly way?
The Adverse Drug Risk Control Panel (ADR CP) withs its e²CP, its MDDI and its High Performance Optimizer technology (5) provides now the solution to answer all the questions brought up, to overcome the inadequacies of traditional drug interaction checkers, and to initiate the paradigm change needed. It provides the all in one; overview of drug risks in patient medications. It reconciles the world of multiple drug drug interactions (MDDI) with the world of vital signs and serious adverse drug effects. It opens the door to fast and comprehensive optimization of multiple drug prescriptions. Thereby prescribers can even in complex scenarios of polypharmacy recognize at one glance where severe hazards of a medication may exist and harm the patient and how to find alternatives to avoid them. Please have a look at the following case study:
Please visit us for more information on scholzdatabank.com or write to [email protected] and make SCHOLZ DataBank the dual circuit brake of your clinical software!
Literature
- SCHOLZ DataBank 2025: description of basic drug database features and numbers
- Nicholas Favatella, David Dalton, Wonkyung Byon, Samira J.Merali, and Christian Klem; Clinical Implications of Co-administering Apixaban with Key Interacting Medications; Clinical Pharmacology in Drug Development 2024, 13(9) 961–973
- Jonathan H Watanabe 1, Terry McInnis 2, Jan D Hirsch 1
Cost of Prescription Drug-Related Morbidity and Mortality
Ann Pharmacother. 2018 Sep;52(9):829-837.
doi: 10.1177/1060028018765159. Epub 2018 Mar 26. PMID: 29577766
4. Polypharmacy (By staff) US Pharm. 2017;42(6):13-14.
5. E²CP = electronic express Chromapictography; MDDI = Multi Drug Drug Interactions; HP Optimizer = High Performance Optimizer for multiple drug prescriptions; for more details ask for the SCHOLZ DataBank User Manual
4. Polypharmacy (By staff) US Pharm. 2017;42(6):13-14.
5. E²CP = electronic express Chromapictography; MDDI = Multi Drug Drug Interactions; HP Optimizer = High Performance Optimizer for multiple drug prescriptions; for more details ask for the SCHOLZ DataBank User Manual