BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Vrije Universiteit Amsterdam//NONSGML v1.0//EN NAME:PhD defence A.J.P. Hopstaken METHOD:PUBLISH BEGIN:VEVENT DTSTART:20260703T134500 DTEND:20260703T151500 DTSTAMP:20260703T134500 UID:phd-defence-a-j-p-hopstaken@8F96275E-9F55-4B3F-A143-836282E12573 CREATED:20260924T220953 LOCATION:Main building VU, 1105, Aula, De Boelelaan, 1081 HV, Amsterdam SUMMARY:PhD defence A.J.P. Hopstaken X-ALT-DESC;FMTTYPE=text/html:

Beyond Binding: Biol ogically Active Cyclic Peptides from mRNA Display

Research accelerates search for new drugs against COVID-19 and malar ia

When discovering new drug s, scientists often look for molecules that bind to proteins involved in a disease. The problem is that many of these molecules do stick t o a protein, but ultimately have no therapeutic effect. Hopstaken the refore focused on how precisely the active molecules can be extracted from huge molecular collections.

The results show that several new selection methods are successful. In one study, Hopstaken develo ped a collection of molecules based on a previously discovered COVID- 19 drug. From this, a new candidate was selected that proved to be ab out a thousand times more potent than the original drug. In animal ex periments, it also provided protection against infection.

The r esearch also yielded promising results in the field of malaria. Using a modified selection technique, Hopstaken identified new drug candid ates from a collection of more than a trillion molecules. By first ex cluding undesirable molecules, he was able to focus on compounds with the highest likelihood of efficacy. The candidates found work throug h a different mechanism than existing malaria drugs, offering perspec tive in the fight against increasing drug resistance.

In additi on, Hopstaken developed an approach in which millions of potential dr ugs can be tested simultaneously for their biological activity. By co mbining this technique with existing selection methods, not only can molecules that bind to a target be found, but also directly compounds that actually have an effect. This makes the drug development proces s more efficient and accurate.

The improved COVID-19 drug is cu rrently being further developed by the company VirXcel, with the goal of bringing a protective nasal spray to market. The initial applicat ion targets people with weakened immune systems, but in time the drug could have broader applications.

The discovered malaria candid ates are also being further investigated by researchers and the Biome dical Primate Research Center. If efficacy is confirmed, they could c ontribute to new treatments against a disease that kills hundreds of thousands each year.

According to Hopstaken, the work shows tha t smart selection techniques increase the chances of finding effectiv e drugs in huge molecular databases. This will allow the pharmaceutic al industry in the future to develop faster and more targeted new tre atments for infectious diseases and other conditions for which insuff icient therapies are still available.

More information about th e dissertation

DESCRIPTION: Beyond Binding: Biologically Active Cyclic Peptides from mRNA Display

Research accelerates search for new drugs ag ainst COVID-19 and malaria

The study by Twan Hopstaken, researcher in the field of peptide drug development, shows that it is possible to select promising drug candidates from collections of mor e than a trillion random molecules in a much more targeted manner tha n before. This could significantly accelerate the development of new drugs for diseases for which no good treatment yet exists.When discov ering new drugs, scientists often look for molecules that bind to pro teins involved in a disease. The problem is that many of these molecu les do stick to a protein, but ultimately have no therapeutic effect. Hopstaken therefore focused on how precisely the active molecules ca n be extracted from huge molecular collections.The results show that several new selection methods are successful. In one study, Hopstaken developed a collection of molecules based on a previously discovered COVID-19 drug. From this, a new candidate was selected that proved t o be about a thousand times more potent than the original drug. In an imal experiments, it also provided protection against infection.The r esearch also yielded promising results in the field of malaria. Using a modified selection technique, Hopstaken identified new drug candid ates from a collection of more than a trillion molecules. By first ex cluding undesirable molecules, he was able to focus on compounds with the highest likelihood of efficacy. The candidates found work throug h a different mechanism than existing malaria drugs, offering perspec tive in the fight against increasing drug resistance.In addition, Hop staken developed an approach in which millions of potential drugs can be tested simultaneously for their biological activity. By combining this technique with existing selection methods, not only can molecul es that bind to a target be found, but also directly compounds that a ctually have an effect. This makes the drug development process more efficient and accurate.The improved COVID-19 drug is currently being further developed by the company VirXcel, with the goal of bringing a protective nasal spray to market. The initial application targets pe ople with weakened immune systems, but in time the drug could have br oader applications.The discovered malaria candidates are also being f urther investigated by researchers and the Biomedical Primate Researc h Center. If efficacy is confirmed, they could contribute to new trea tments against a disease that kills hundreds of thousands each year.A ccording to Hopstaken, the work shows that smart selection techniques increase the chances of finding effective drugs in huge molecular da tabases. This will allow the pharmaceutical industry in the future to develop faster and more targeted new treatments for infectious disea ses and other conditions for which insufficient therapies are still a vailable.More information about the dissertation END:VEVENT END:VCALENDAR